{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 项目实现\n",
    "## 获取数据、基本数据信息查看"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"./data/train_V2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
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       "      <th>matchId</th>\n",
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       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>...</th>\n",
       "      <th>revives</th>\n",
       "      <th>rideDistance</th>\n",
       "      <th>roadKills</th>\n",
       "      <th>swimDistance</th>\n",
       "      <th>teamKills</th>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <th>walkDistance</th>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <th>winPoints</th>\n",
       "      <th>winPlacePerc</th>\n",
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       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>161.80</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0</td>\n",
       "      <td>75</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0.1875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
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      ],
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       "               Id         groupId         matchId  assists  boosts  \\\n",
       "0  7f96b2f878858a  4d4b580de459be  a10357fd1a4a91        0       0   \n",
       "1  eef90569b9d03c  684d5656442f9e  aeb375fc57110c        0       0   \n",
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       "4  315c96c26c9aac  de04010b3458dd  6dc8ff871e21e6        0       0   \n",
       "\n",
       "   damageDealt  DBNOs  headshotKills  heals  killPlace      ...       revives  \\\n",
       "0         0.00      0              0      0         60      ...             0   \n",
       "1        91.47      0              0      0         57      ...             0   \n",
       "2        68.00      0              0      0         47      ...             0   \n",
       "3        32.90      0              0      0         75      ...             0   \n",
       "4       100.00      0              0      0         45      ...             0   \n",
       "\n",
       "   rideDistance  roadKills  swimDistance  teamKills vehicleDestroys  \\\n",
       "0        0.0000          0          0.00          0               0   \n",
       "1        0.0045          0         11.04          0               0   \n",
       "2        0.0000          0          0.00          0               0   \n",
       "3        0.0000          0          0.00          0               0   \n",
       "4        0.0000          0          0.00          0               0   \n",
       "\n",
       "   walkDistance  weaponsAcquired  winPoints  winPlacePerc  \n",
       "0        244.80                1       1466        0.4444  \n",
       "1       1434.00                5          0        0.6400  \n",
       "2        161.80                2          0        0.7755  \n",
       "3        202.70                3          0        0.1667  \n",
       "4         49.75                2          0        0.1875  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>4446964</th>\n",
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       "                     Id         groupId         matchId  assists  boosts  \\\n",
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       "4446964  cc032cdd73b7ac  c2223f35411394  c9c701d0ad758a        0       4   \n",
       "4446965  0d8e7ed728b6fd  8c74f72fedf5ff  62a16aabcc095c        0       2   \n",
       "\n",
       "         damageDealt  DBNOs  headshotKills  heals  killPlace      ...       \\\n",
       "4446961         0.00      0              0      0         74      ...        \n",
       "4446962        44.15      0              0      0         69      ...        \n",
       "4446963        59.06      0              0      0         66      ...        \n",
       "4446964       180.40      1              1      2         11      ...        \n",
       "4446965       268.00      0              0      1         18      ...        \n",
       "\n",
       "         revives  rideDistance  roadKills  swimDistance  teamKills  \\\n",
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       "4446964        2           0.0          0         0.000          0   \n",
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       "\n",
       "        vehicleDestroys  walkDistance  weaponsAcquired  winPoints  \\\n",
       "4446961               0        1019.0                3       1507   \n",
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       "4446963               0         788.7                4          0   \n",
       "4446964               0        2748.0                8          0   \n",
       "4446965               0        1244.0                5          0   \n",
       "\n",
       "         winPlacePerc  \n",
       "4446961        0.1786  \n",
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       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>4.446966e+06</td>\n",
       "      <td>4.446966e+06</td>\n",
       "      <td>4.446966e+06</td>\n",
       "      <td>4.446966e+06</td>\n",
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       "      <td>4.446966e+06</td>\n",
       "      <td>4.446966e+06</td>\n",
       "      <td>4.446966e+06</td>\n",
       "      <td>4.446966e+06</td>\n",
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       "      <td>4.446966e+06</td>\n",
       "      <td>4.446966e+06</td>\n",
       "      <td>4.446965e+06</td>\n",
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       "      <th>mean</th>\n",
       "      <td>2.338149e-01</td>\n",
       "      <td>1.106908e+00</td>\n",
       "      <td>1.307171e+02</td>\n",
       "      <td>6.578755e-01</td>\n",
       "      <td>2.268196e-01</td>\n",
       "      <td>1.370147e+00</td>\n",
       "      <td>4.759935e+01</td>\n",
       "      <td>5.050060e+02</td>\n",
       "      <td>9.247833e-01</td>\n",
       "      <td>5.439551e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.646590e-01</td>\n",
       "      <td>6.061157e+02</td>\n",
       "      <td>3.496091e-03</td>\n",
       "      <td>4.509322e+00</td>\n",
       "      <td>2.386841e-02</td>\n",
       "      <td>7.918208e-03</td>\n",
       "      <td>1.154218e+03</td>\n",
       "      <td>3.660488e+00</td>\n",
       "      <td>6.064601e+02</td>\n",
       "      <td>4.728216e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.885731e-01</td>\n",
       "      <td>1.715794e+00</td>\n",
       "      <td>1.707806e+02</td>\n",
       "      <td>1.145743e+00</td>\n",
       "      <td>6.021553e-01</td>\n",
       "      <td>2.679982e+00</td>\n",
       "      <td>2.746294e+01</td>\n",
       "      <td>6.275049e+02</td>\n",
       "      <td>1.558445e+00</td>\n",
       "      <td>7.109721e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>4.721671e-01</td>\n",
       "      <td>1.498344e+03</td>\n",
       "      <td>7.337297e-02</td>\n",
       "      <td>3.050220e+01</td>\n",
       "      <td>1.673935e-01</td>\n",
       "      <td>9.261157e-02</td>\n",
       "      <td>1.183497e+03</td>\n",
       "      <td>2.456544e+00</td>\n",
       "      <td>7.397004e+02</td>\n",
       "      <td>3.074050e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.551000e+02</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>8.424000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.856000e+02</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.583000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.860000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>7.100000e+01</td>\n",
       "      <td>1.172000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.909750e-01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.976000e+03</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.495000e+03</td>\n",
       "      <td>7.407000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>3.300000e+01</td>\n",
       "      <td>6.616000e+03</td>\n",
       "      <td>5.300000e+01</td>\n",
       "      <td>6.400000e+01</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>1.010000e+02</td>\n",
       "      <td>2.170000e+03</td>\n",
       "      <td>7.200000e+01</td>\n",
       "      <td>2.000000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>3.900000e+01</td>\n",
       "      <td>4.071000e+04</td>\n",
       "      <td>1.800000e+01</td>\n",
       "      <td>3.823000e+03</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>2.578000e+04</td>\n",
       "      <td>2.360000e+02</td>\n",
       "      <td>2.013000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            assists        boosts   damageDealt         DBNOs  headshotKills  \\\n",
       "count  4.446966e+06  4.446966e+06  4.446966e+06  4.446966e+06   4.446966e+06   \n",
       "mean   2.338149e-01  1.106908e+00  1.307171e+02  6.578755e-01   2.268196e-01   \n",
       "std    5.885731e-01  1.715794e+00  1.707806e+02  1.145743e+00   6.021553e-01   \n",
       "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   0.000000e+00   \n",
       "25%    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   0.000000e+00   \n",
       "50%    0.000000e+00  0.000000e+00  8.424000e+01  0.000000e+00   0.000000e+00   \n",
       "75%    0.000000e+00  2.000000e+00  1.860000e+02  1.000000e+00   0.000000e+00   \n",
       "max    2.200000e+01  3.300000e+01  6.616000e+03  5.300000e+01   6.400000e+01   \n",
       "\n",
       "              heals     killPlace    killPoints         kills   killStreaks  \\\n",
       "count  4.446966e+06  4.446966e+06  4.446966e+06  4.446966e+06  4.446966e+06   \n",
       "mean   1.370147e+00  4.759935e+01  5.050060e+02  9.247833e-01  5.439551e-01   \n",
       "std    2.679982e+00  2.746294e+01  6.275049e+02  1.558445e+00  7.109721e-01   \n",
       "min    0.000000e+00  1.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%    0.000000e+00  2.400000e+01  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "50%    0.000000e+00  4.700000e+01  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "75%    2.000000e+00  7.100000e+01  1.172000e+03  1.000000e+00  1.000000e+00   \n",
       "max    8.000000e+01  1.010000e+02  2.170000e+03  7.200000e+01  2.000000e+01   \n",
       "\n",
       "           ...            revives  rideDistance     roadKills  swimDistance  \\\n",
       "count      ...       4.446966e+06  4.446966e+06  4.446966e+06  4.446966e+06   \n",
       "mean       ...       1.646590e-01  6.061157e+02  3.496091e-03  4.509322e+00   \n",
       "std        ...       4.721671e-01  1.498344e+03  7.337297e-02  3.050220e+01   \n",
       "min        ...       0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "25%        ...       0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "50%        ...       0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
       "75%        ...       0.000000e+00  1.909750e-01  0.000000e+00  0.000000e+00   \n",
       "max        ...       3.900000e+01  4.071000e+04  1.800000e+01  3.823000e+03   \n",
       "\n",
       "          teamKills  vehicleDestroys  walkDistance  weaponsAcquired  \\\n",
       "count  4.446966e+06     4.446966e+06  4.446966e+06     4.446966e+06   \n",
       "mean   2.386841e-02     7.918208e-03  1.154218e+03     3.660488e+00   \n",
       "std    1.673935e-01     9.261157e-02  1.183497e+03     2.456544e+00   \n",
       "min    0.000000e+00     0.000000e+00  0.000000e+00     0.000000e+00   \n",
       "25%    0.000000e+00     0.000000e+00  1.551000e+02     2.000000e+00   \n",
       "50%    0.000000e+00     0.000000e+00  6.856000e+02     3.000000e+00   \n",
       "75%    0.000000e+00     0.000000e+00  1.976000e+03     5.000000e+00   \n",
       "max    1.200000e+01     5.000000e+00  2.578000e+04     2.360000e+02   \n",
       "\n",
       "          winPoints  winPlacePerc  \n",
       "count  4.446966e+06  4.446965e+06  \n",
       "mean   6.064601e+02  4.728216e-01  \n",
       "std    7.397004e+02  3.074050e-01  \n",
       "min    0.000000e+00  0.000000e+00  \n",
       "25%    0.000000e+00  2.000000e-01  \n",
       "50%    0.000000e+00  4.583000e-01  \n",
       "75%    1.495000e+03  7.407000e-01  \n",
       "max    2.013000e+03  1.000000e+00  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4446966 entries, 0 to 4446965\n",
      "Data columns (total 29 columns):\n",
      "Id                 object\n",
      "groupId            object\n",
      "matchId            object\n",
      "assists            int64\n",
      "boosts             int64\n",
      "damageDealt        float64\n",
      "DBNOs              int64\n",
      "headshotKills      int64\n",
      "heals              int64\n",
      "killPlace          int64\n",
      "killPoints         int64\n",
      "kills              int64\n",
      "killStreaks        int64\n",
      "longestKill        float64\n",
      "matchDuration      int64\n",
      "matchType          object\n",
      "maxPlace           int64\n",
      "numGroups          int64\n",
      "rankPoints         int64\n",
      "revives            int64\n",
      "rideDistance       float64\n",
      "roadKills          int64\n",
      "swimDistance       float64\n",
      "teamKills          int64\n",
      "vehicleDestroys    int64\n",
      "walkDistance       float64\n",
      "weaponsAcquired    int64\n",
      "winPoints          int64\n",
      "winPlacePerc       float64\n",
      "dtypes: float64(6), int64(19), object(4)\n",
      "memory usage: 983.9+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4446966, 29)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看一共要多少条数据\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47965,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 有多少场比赛\n",
    "np.unique(train[\"matchId\"]).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2026745,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 有多少支队伍\n",
    "np.unique(train[\"groupId\"]).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据基本处理\n",
    "### 数据缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 False\n",
       "groupId            False\n",
       "matchId            False\n",
       "assists            False\n",
       "boosts             False\n",
       "damageDealt        False\n",
       "DBNOs              False\n",
       "headshotKills      False\n",
       "heals              False\n",
       "killPlace          False\n",
       "killPoints         False\n",
       "kills              False\n",
       "killStreaks        False\n",
       "longestKill        False\n",
       "matchDuration      False\n",
       "matchType          False\n",
       "maxPlace           False\n",
       "numGroups          False\n",
       "rankPoints         False\n",
       "revives            False\n",
       "rideDistance       False\n",
       "roadKills          False\n",
       "swimDistance       False\n",
       "teamKills          False\n",
       "vehicleDestroys    False\n",
       "walkDistance       False\n",
       "weaponsAcquired    False\n",
       "winPoints          False\n",
       "winPlacePerc        True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 判断哪列有缺失值,发现只有winPlacePerc有 \n",
    "np.any(train.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</th>\n",
       "      <th>assists</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>...</th>\n",
       "      <th>revives</th>\n",
       "      <th>rideDistance</th>\n",
       "      <th>roadKills</th>\n",
       "      <th>swimDistance</th>\n",
       "      <th>teamKills</th>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <th>walkDistance</th>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <th>winPoints</th>\n",
       "      <th>winPlacePerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2744604</th>\n",
       "      <td>f70c74418bb064</td>\n",
       "      <td>12dfbede33f92b</td>\n",
       "      <td>224a123c53e008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Id         groupId         matchId  assists  boosts  \\\n",
       "2744604  f70c74418bb064  12dfbede33f92b  224a123c53e008        0       0   \n",
       "\n",
       "         damageDealt  DBNOs  headshotKills  heals  killPlace      ...       \\\n",
       "2744604          0.0      0              0      0          1      ...        \n",
       "\n",
       "         revives  rideDistance  roadKills  swimDistance  teamKills  \\\n",
       "2744604        0           0.0          0           0.0          0   \n",
       "\n",
       "        vehicleDestroys  walkDistance  weaponsAcquired  winPoints  \\\n",
       "2744604               0           0.0                0          0   \n",
       "\n",
       "         winPlacePerc  \n",
       "2744604           NaN  \n",
       "\n",
       "[1 rows x 29 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查找缺失值\n",
    "train[train[\"winPlacePerc\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除\n",
    "train = train.drop(2744604)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4446965, 29)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征数据规范化处理\n",
    "#### 查看每场比赛参加的人数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "count = train.groupby(\"matchId\")[\"matchId\"].transform(\"count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"playersJoined\"] = count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</th>\n",
       "      <th>assists</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>...</th>\n",
       "      <th>rideDistance</th>\n",
       "      <th>roadKills</th>\n",
       "      <th>swimDistance</th>\n",
       "      <th>teamKills</th>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <th>walkDistance</th>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <th>winPoints</th>\n",
       "      <th>winPlacePerc</th>\n",
       "      <th>playersJoined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7f96b2f878858a</td>\n",
       "      <td>4d4b580de459be</td>\n",
       "      <td>a10357fd1a4a91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1466</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eef90569b9d03c</td>\n",
       "      <td>684d5656442f9e</td>\n",
       "      <td>aeb375fc57110c</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91.47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0045</td>\n",
       "      <td>0</td>\n",
       "      <td>11.04</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1434.00</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6400</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1eaf90ac73de72</td>\n",
       "      <td>6a4a42c3245a74</td>\n",
       "      <td>110163d8bb94ae</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>161.80</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7755</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4616d365dd2853</td>\n",
       "      <td>a930a9c79cd721</td>\n",
       "      <td>f1f1f4ef412d7e</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202.70</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1667</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>315c96c26c9aac</td>\n",
       "      <td>de04010b3458dd</td>\n",
       "      <td>6dc8ff871e21e6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.75</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id         groupId         matchId  assists  boosts  \\\n",
       "0  7f96b2f878858a  4d4b580de459be  a10357fd1a4a91        0       0   \n",
       "1  eef90569b9d03c  684d5656442f9e  aeb375fc57110c        0       0   \n",
       "2  1eaf90ac73de72  6a4a42c3245a74  110163d8bb94ae        1       0   \n",
       "3  4616d365dd2853  a930a9c79cd721  f1f1f4ef412d7e        0       0   \n",
       "4  315c96c26c9aac  de04010b3458dd  6dc8ff871e21e6        0       0   \n",
       "\n",
       "   damageDealt  DBNOs  headshotKills  heals  killPlace      ...        \\\n",
       "0         0.00      0              0      0         60      ...         \n",
       "1        91.47      0              0      0         57      ...         \n",
       "2        68.00      0              0      0         47      ...         \n",
       "3        32.90      0              0      0         75      ...         \n",
       "4       100.00      0              0      0         45      ...         \n",
       "\n",
       "   rideDistance  roadKills  swimDistance  teamKills  vehicleDestroys  \\\n",
       "0        0.0000          0          0.00          0                0   \n",
       "1        0.0045          0         11.04          0                0   \n",
       "2        0.0000          0          0.00          0                0   \n",
       "3        0.0000          0          0.00          0                0   \n",
       "4        0.0000          0          0.00          0                0   \n",
       "\n",
       "  walkDistance  weaponsAcquired  winPoints  winPlacePerc  playersJoined  \n",
       "0       244.80                1       1466        0.4444             96  \n",
       "1      1434.00                5          0        0.6400             91  \n",
       "2       161.80                2          0        0.7755             98  \n",
       "3       202.70                3          0        0.1667             91  \n",
       "4        49.75                2          0        0.1875             97  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1206365    2\n",
       "2109739    2\n",
       "3956552    5\n",
       "3620228    5\n",
       "696000     5\n",
       "Name: playersJoined, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"playersJoined\"].sort_values().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8))\n",
    "sns.countplot(train[\"playersJoined\"])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train[train[\"playersJoined\"]>=75][\"playersJoined\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8))\n",
    "sns.countplot(train[train[\"playersJoined\"]>=75][\"playersJoined\"])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 规范化输出部分数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"killsNorm\"] = train[\"kills\"] * ((100-train[\"playersJoined\"])/100+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"damageDealtNorm\"] = train[\"damageDealt\"] * ((100-train[\"playersJoined\"])/100+1)\n",
    "train[\"maxPlaceNorm\"] = train[\"maxPlace\"] * ((100-train[\"playersJoined\"])/100+1)\n",
    "train[\"matchDurationNorm\"] = train[\"matchDuration\"] * ((100-train[\"playersJoined\"])/100+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</th>\n",
       "      <th>assists</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>...</th>\n",
       "      <th>vehicleDestroys</th>\n",
       "      <th>walkDistance</th>\n",
       "      <th>weaponsAcquired</th>\n",
       "      <th>winPoints</th>\n",
       "      <th>winPlacePerc</th>\n",
       "      <th>playersJoined</th>\n",
       "      <th>killsNorm</th>\n",
       "      <th>damageDealtNorm</th>\n",
       "      <th>maxPlaceNorm</th>\n",
       "      <th>matchDurationNorm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7f96b2f878858a</td>\n",
       "      <td>4d4b580de459be</td>\n",
       "      <td>a10357fd1a4a91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>244.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1466</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>29.12</td>\n",
       "      <td>1358.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eef90569b9d03c</td>\n",
       "      <td>684d5656442f9e</td>\n",
       "      <td>aeb375fc57110c</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91.47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1434.00</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6400</td>\n",
       "      <td>91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>99.7023</td>\n",
       "      <td>28.34</td>\n",
       "      <td>1936.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1eaf90ac73de72</td>\n",
       "      <td>6a4a42c3245a74</td>\n",
       "      <td>110163d8bb94ae</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>161.80</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7755</td>\n",
       "      <td>98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>69.3600</td>\n",
       "      <td>51.00</td>\n",
       "      <td>1344.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4616d365dd2853</td>\n",
       "      <td>a930a9c79cd721</td>\n",
       "      <td>f1f1f4ef412d7e</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>202.70</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1667</td>\n",
       "      <td>91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35.8610</td>\n",
       "      <td>33.79</td>\n",
       "      <td>1565.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>315c96c26c9aac</td>\n",
       "      <td>de04010b3458dd</td>\n",
       "      <td>6dc8ff871e21e6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>49.75</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>97</td>\n",
       "      <td>1.03</td>\n",
       "      <td>103.0000</td>\n",
       "      <td>99.91</td>\n",
       "      <td>1466.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id         groupId         matchId  assists  boosts  \\\n",
       "0  7f96b2f878858a  4d4b580de459be  a10357fd1a4a91        0       0   \n",
       "1  eef90569b9d03c  684d5656442f9e  aeb375fc57110c        0       0   \n",
       "2  1eaf90ac73de72  6a4a42c3245a74  110163d8bb94ae        1       0   \n",
       "3  4616d365dd2853  a930a9c79cd721  f1f1f4ef412d7e        0       0   \n",
       "4  315c96c26c9aac  de04010b3458dd  6dc8ff871e21e6        0       0   \n",
       "\n",
       "   damageDealt  DBNOs  headshotKills  heals  killPlace        ...          \\\n",
       "0         0.00      0              0      0         60        ...           \n",
       "1        91.47      0              0      0         57        ...           \n",
       "2        68.00      0              0      0         47        ...           \n",
       "3        32.90      0              0      0         75        ...           \n",
       "4       100.00      0              0      0         45        ...           \n",
       "\n",
       "   vehicleDestroys  walkDistance  weaponsAcquired  winPoints  winPlacePerc  \\\n",
       "0                0        244.80                1       1466        0.4444   \n",
       "1                0       1434.00                5          0        0.6400   \n",
       "2                0        161.80                2          0        0.7755   \n",
       "3                0        202.70                3          0        0.1667   \n",
       "4                0         49.75                2          0        0.1875   \n",
       "\n",
       "  playersJoined  killsNorm  damageDealtNorm  maxPlaceNorm  matchDurationNorm  \n",
       "0            96       0.00           0.0000         29.12            1358.24  \n",
       "1            91       0.00          99.7023         28.34            1936.93  \n",
       "2            98       0.00          69.3600         51.00            1344.36  \n",
       "3            91       0.00          35.8610         33.79            1565.24  \n",
       "4            97       1.03         103.0000         99.91            1466.72  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>kills</th>\n",
       "      <th>killsNorm</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>damageDealtNorm</th>\n",
       "      <th>maxPlace</th>\n",
       "      <th>maxPlaceNorm</th>\n",
       "      <th>matchDuration</th>\n",
       "      <th>matchDurationNorm</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7f96b2f878858a</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>28</td>\n",
       "      <td>29.12</td>\n",
       "      <td>1306</td>\n",
       "      <td>1358.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eef90569b9d03c</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>91.470</td>\n",
       "      <td>99.70230</td>\n",
       "      <td>26</td>\n",
       "      <td>28.34</td>\n",
       "      <td>1777</td>\n",
       "      <td>1936.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1eaf90ac73de72</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>68.000</td>\n",
       "      <td>69.36000</td>\n",
       "      <td>50</td>\n",
       "      <td>51.00</td>\n",
       "      <td>1318</td>\n",
       "      <td>1344.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4616d365dd2853</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>32.900</td>\n",
       "      <td>35.86100</td>\n",
       "      <td>31</td>\n",
       "      <td>33.79</td>\n",
       "      <td>1436</td>\n",
       "      <td>1565.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>315c96c26c9aac</td>\n",
       "      <td>1</td>\n",
       "      <td>1.03</td>\n",
       "      <td>100.000</td>\n",
       "      <td>103.00000</td>\n",
       "      <td>97</td>\n",
       "      <td>99.91</td>\n",
       "      <td>1424</td>\n",
       "      <td>1466.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ff79c12f326506</td>\n",
       "      <td>1</td>\n",
       "      <td>1.05</td>\n",
       "      <td>100.000</td>\n",
       "      <td>105.00000</td>\n",
       "      <td>28</td>\n",
       "      <td>29.40</td>\n",
       "      <td>1395</td>\n",
       "      <td>1464.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>95959be0e21ca3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>28</td>\n",
       "      <td>28.84</td>\n",
       "      <td>1316</td>\n",
       "      <td>1355.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>311b84c6ff4390</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.538</td>\n",
       "      <td>8.87952</td>\n",
       "      <td>96</td>\n",
       "      <td>99.84</td>\n",
       "      <td>1967</td>\n",
       "      <td>2045.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1a68204ccf9891</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>51.600</td>\n",
       "      <td>53.14800</td>\n",
       "      <td>28</td>\n",
       "      <td>28.84</td>\n",
       "      <td>1375</td>\n",
       "      <td>1416.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>e5bb5a43587253</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.270</td>\n",
       "      <td>38.38810</td>\n",
       "      <td>29</td>\n",
       "      <td>29.87</td>\n",
       "      <td>1930</td>\n",
       "      <td>1987.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2b574d43972813</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.380</td>\n",
       "      <td>28.66380</td>\n",
       "      <td>29</td>\n",
       "      <td>29.29</td>\n",
       "      <td>1811</td>\n",
       "      <td>1829.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id  kills  killsNorm  damageDealt  damageDealtNorm  maxPlace  \\\n",
       "0   7f96b2f878858a      0       0.00        0.000          0.00000        28   \n",
       "1   eef90569b9d03c      0       0.00       91.470         99.70230        26   \n",
       "2   1eaf90ac73de72      0       0.00       68.000         69.36000        50   \n",
       "3   4616d365dd2853      0       0.00       32.900         35.86100        31   \n",
       "4   315c96c26c9aac      1       1.03      100.000        103.00000        97   \n",
       "5   ff79c12f326506      1       1.05      100.000        105.00000        28   \n",
       "6   95959be0e21ca3      0       0.00        0.000          0.00000        28   \n",
       "7   311b84c6ff4390      0       0.00        8.538          8.87952        96   \n",
       "8   1a68204ccf9891      0       0.00       51.600         53.14800        28   \n",
       "9   e5bb5a43587253      0       0.00       37.270         38.38810        29   \n",
       "10  2b574d43972813      0       0.00       28.380         28.66380        29   \n",
       "\n",
       "    maxPlaceNorm  matchDuration  matchDurationNorm  \n",
       "0          29.12           1306            1358.24  \n",
       "1          28.34           1777            1936.93  \n",
       "2          51.00           1318            1344.36  \n",
       "3          33.79           1436            1565.24  \n",
       "4          99.91           1424            1466.72  \n",
       "5          29.40           1395            1464.75  \n",
       "6          28.84           1316            1355.48  \n",
       "7          99.84           1967            2045.68  \n",
       "8          28.84           1375            1416.25  \n",
       "9          29.87           1930            1987.90  \n",
       "10         29.29           1811            1829.11  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 比较经过规范化的特征值和原始特征值的值\n",
    "to_show = ['Id', 'kills','killsNorm','damageDealt', 'damageDealtNorm', 'maxPlace', 'maxPlaceNorm', 'matchDuration', 'matchDurationNorm']\n",
    "train[to_show][0:11]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 部分变量合成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"healsandboosts\"] = train[\"heals\"] + train[\"boosts\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>heals</th>\n",
       "      <th>boosts</th>\n",
       "      <th>healsandboosts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4446956</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>4446959</th>\n",
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       "    <tr>\n",
       "      <th>4446960</th>\n",
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       "    <tr>\n",
       "      <th>4446961</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4446962</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4446963</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4446964</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4446965</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         heals  boosts  healsandboosts\n",
       "4446956      1       0               1\n",
       "4446957      0       1               1\n",
       "4446958      0       0               0\n",
       "4446959      0       0               0\n",
       "4446960      0       0               0\n",
       "4446961      0       0               0\n",
       "4446962      0       1               1\n",
       "4446963      0       0               0\n",
       "4446964      2       4               6\n",
       "4446965      1       2               3"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[[\"heals\", \"boosts\", \"healsandboosts\"]].tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 异常值处理\n",
    "#### 异常值处理：删除有击杀，但是完全没有移动的玩家"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"totalDistance\"] = train[\"rideDistance\"] + train[\"walkDistance\"] + train[\"swimDistance\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
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       "               Id         groupId         matchId  assists  boosts  \\\n",
       "0  7f96b2f878858a  4d4b580de459be  a10357fd1a4a91        0       0   \n",
       "1  eef90569b9d03c  684d5656442f9e  aeb375fc57110c        0       0   \n",
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       "4  315c96c26c9aac  de04010b3458dd  6dc8ff871e21e6        0       0   \n",
       "\n",
       "   damageDealt  DBNOs  headshotKills  heals  killPlace        ...          \\\n",
       "0         0.00      0              0      0         60        ...           \n",
       "1        91.47      0              0      0         57        ...           \n",
       "2        68.00      0              0      0         47        ...           \n",
       "3        32.90      0              0      0         75        ...           \n",
       "4       100.00      0              0      0         45        ...           \n",
       "\n",
       "   winPoints  winPlacePerc  playersJoined  killsNorm  damageDealtNorm  \\\n",
       "0       1466        0.4444             96       0.00           0.0000   \n",
       "1          0        0.6400             91       0.00          99.7023   \n",
       "2          0        0.7755             98       0.00          69.3600   \n",
       "3          0        0.1667             91       0.00          35.8610   \n",
       "4          0        0.1875             97       1.03         103.0000   \n",
       "\n",
       "  maxPlaceNorm  matchDurationNorm  healsandboosts  totalDistance  \\\n",
       "0        29.12            1358.24               0       244.8000   \n",
       "1        28.34            1936.93               0      1445.0445   \n",
       "2        51.00            1344.36               0       161.8000   \n",
       "3        33.79            1565.24               0       202.7000   \n",
       "4        99.91            1466.72               0        49.7500   \n",
       "\n",
       "   killwithoutMoving  \n",
       "0              False  \n",
       "1              False  \n",
       "2              False  \n",
       "3              False  \n",
       "4              False  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (train[\"kills\"] > 0) & (train[\"totalDistance\"] == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"killwithoutMoving\"] = (train[\"kills\"] > 0) & (train[\"totalDistance\"] == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                   Id         groupId         matchId  assists  boosts  \\\n",
       "1824   b538d514ef2476  0eb2ce2f43f9d6  35e7d750e442e2        0       0   \n",
       "6673   6d3a61da07b7cb  2d8119b1544f87  904cecf36217df        2       0   \n",
       "11892  550398a8f33db7  c3fd0e2abab0af  db6f6d1f0d4904        2       0   \n",
       "14631  58d690ee461e9d  ea5b6630b33d67  dbf34301df5e53        0       0   \n",
       "15591  49b61fc963d632  0f5c5f19d9cc21  904cecf36217df        0       0   \n",
       "\n",
       "       damageDealt  DBNOs  headshotKills  heals  killPlace        ...          \\\n",
       "1824         593.0      0              0      3         18        ...           \n",
       "6673         346.6      0              0      6         33        ...           \n",
       "11892       1750.0      0              4      5          3        ...           \n",
       "14631        157.8      0              0      0         69        ...           \n",
       "15591        100.0      0              1      0         37        ...           \n",
       "\n",
       "       winPoints  winPlacePerc  playersJoined  killsNorm  damageDealtNorm  \\\n",
       "1824           0        0.8571             58       8.52          842.060   \n",
       "6673           0        0.6000             42       4.74          547.628   \n",
       "11892          0        0.8947             21      35.80         3132.500   \n",
       "14631       1500        0.0000             73       1.27          200.406   \n",
       "15591          0        0.3000             42       1.58          158.000   \n",
       "\n",
       "      maxPlaceNorm  matchDurationNorm  healsandboosts  totalDistance  \\\n",
       "1824         21.30             842.06               3            0.0   \n",
       "6673         17.38            2834.52               6            0.0   \n",
       "11892        35.80            1607.42               5            0.0   \n",
       "14631        24.13            1014.73               0            0.0   \n",
       "15591        17.38            2834.52               0            0.0   \n",
       "\n",
       "       killwithoutMoving  \n",
       "1824                True  \n",
       "6673                True  \n",
       "11892               True  \n",
       "14631               True  \n",
       "15591               True  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train[\"killwithoutMoving\"] == True].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1535, 37)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train[\"killwithoutMoving\"] == True].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   1824,    6673,   11892,   14631,   15591,   20881,   23298,\n",
       "              24640,   25659,   30079,\n",
       "            ...\n",
       "            4426500, 4429697, 4432954, 4436511, 4437516, 4440232, 4440898,\n",
       "            4440927, 4441511, 4446682],\n",
       "           dtype='int64', length=1535)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train[\"killwithoutMoving\"] == True].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(train[train[\"killwithoutMoving\"] == True].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4445430, 37)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 异常值处理：删除驾车杀敌数异常的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train[\"roadKills\"] > 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(train[train[\"roadKills\"] > 10].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4445426, 37)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 异常值处理：删除玩家在一局中杀敌数超过30人的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>assists</th>\n",
       "      <th>boosts</th>\n",
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       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>...</th>\n",
       "      <th>winPoints</th>\n",
       "      <th>winPlacePerc</th>\n",
       "      <th>playersJoined</th>\n",
       "      <th>killsNorm</th>\n",
       "      <th>damageDealtNorm</th>\n",
       "      <th>maxPlaceNorm</th>\n",
       "      <th>matchDurationNorm</th>\n",
       "      <th>healsandboosts</th>\n",
       "      <th>totalDistance</th>\n",
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       "      <td>3398.22</td>\n",
       "      <td>7</td>\n",
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       "      <th>160254</th>\n",
       "      <td>15622257cb44e2</td>\n",
       "      <td>1a513eeecfe724</td>\n",
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       "      <td>1500</td>\n",
       "      <td>1.0000</td>\n",
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       "      <td>1.0000</td>\n",
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       "      <td>5993.19</td>\n",
       "      <td>17.01</td>\n",
       "      <td>3394.44</td>\n",
       "      <td>15</td>\n",
       "      <td>71.51</td>\n",
       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Id         groupId         matchId  assists  boosts  \\\n",
       "57978   9d8253e21ccbbd  ef7135ed856cd8  37f05e2a01015f        9       0   \n",
       "87793   45f76442384931  b3627758941d34  37f05e2a01015f        8       0   \n",
       "156599  746aa7eabf7c86  5723e7d8250da3  f900de1ec39fa5       21       0   \n",
       "160254  15622257cb44e2  1a513eeecfe724  db413c7c48292c        1       0   \n",
       "180189  1355613d43e2d0  f863cd38c61dbf  39c442628f5df5        5       0   \n",
       "\n",
       "        damageDealt  DBNOs  headshotKills  heals  killPlace  \\\n",
       "57978        3725.0      0              7      0          2   \n",
       "87793        3087.0      0              8     27          3   \n",
       "156599       5479.0      0             12      7          4   \n",
       "160254       4033.0      0             40      0          1   \n",
       "180189       3171.0      0              6     15          1   \n",
       "\n",
       "              ...          winPoints  winPlacePerc  playersJoined  killsNorm  \\\n",
       "57978         ...               1500        0.8571             16      64.40   \n",
       "87793         ...               1500        1.0000             16      57.04   \n",
       "156599        ...                  0        0.7000             11      90.72   \n",
       "160254        ...               1500        1.0000             62      57.96   \n",
       "180189        ...                  0        1.0000             11      66.15   \n",
       "\n",
       "        damageDealtNorm maxPlaceNorm  matchDurationNorm  healsandboosts  \\\n",
       "57978           6854.00        14.72            3308.32               0   \n",
       "87793           5680.08        14.72            3308.32              27   \n",
       "156599         10355.31        20.79            3398.22               7   \n",
       "160254          5565.54        11.04            1164.72               0   \n",
       "180189          5993.19        17.01            3394.44              15   \n",
       "\n",
       "        totalDistance  killwithoutMoving  \n",
       "57978           48.82              False  \n",
       "87793          780.70              False  \n",
       "156599          23.71              False  \n",
       "160254         718.30              False  \n",
       "180189          71.51              False  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train[\"kills\"] > 30].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4445331, 37)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(train[train[\"kills\"] > 30].index, inplace=True)\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 异常值处理：删除爆头率异常数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    NaN\n",
       "2    NaN\n",
       "3    NaN\n",
       "4    0.0\n",
       "Name: headshot_rate, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"headshot_rate\"] = train[\"headshotKills\"]/train[\"kills\"]\n",
    "train[\"headshot_rate\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"headshot_rate\"] = train[\"headshot_rate\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
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       "               Id         groupId         matchId  assists  boosts  \\\n",
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       "\n",
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       "2        68.00      0              0      0         47      ...         \n",
       "3        32.90      0              0      0         75      ...         \n",
       "4       100.00      0              0      0         45      ...         \n",
       "\n",
       "   winPlacePerc  playersJoined  killsNorm  damageDealtNorm  maxPlaceNorm  \\\n",
       "0        0.4444             96       0.00           0.0000         29.12   \n",
       "1        0.6400             91       0.00          99.7023         28.34   \n",
       "2        0.7755             98       0.00          69.3600         51.00   \n",
       "3        0.1667             91       0.00          35.8610         33.79   \n",
       "4        0.1875             97       1.03         103.0000         99.91   \n",
       "\n",
       "  matchDurationNorm  healsandboosts  totalDistance  killwithoutMoving  \\\n",
       "0           1358.24               0       244.8000              False   \n",
       "1           1936.93               0      1445.0445              False   \n",
       "2           1344.36               0       161.8000              False   \n",
       "3           1565.24               0       202.7000              False   \n",
       "4           1466.72               0        49.7500              False   \n",
       "\n",
       "   headshot_rate  \n",
       "0            0.0  \n",
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       "4            0.0  \n",
       "\n",
       "[5 rows x 38 columns]"
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     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1296.84</td>\n",
       "      <td>28.89</td>\n",
       "      <td>1522.61</td>\n",
       "      <td>3</td>\n",
       "      <td>2939.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>346124</th>\n",
       "      <td>044d18fc42fc75</td>\n",
       "      <td>fc1dbc2df6a887</td>\n",
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       "      <th>871244</th>\n",
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       "      <td>1.0000</td>\n",
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       "      <td>13.26</td>\n",
       "      <td>1392.30</td>\n",
       "      <td>27.54</td>\n",
       "      <td>1280.10</td>\n",
       "      <td>4</td>\n",
       "      <td>2105.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908815</th>\n",
       "      <td>566d8218b705aa</td>\n",
       "      <td>a9b056478d71b2</td>\n",
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       "      <td>0.9630</td>\n",
       "      <td>95</td>\n",
       "      <td>10.50</td>\n",
       "      <td>1611.75</td>\n",
       "      <td>29.40</td>\n",
       "      <td>1929.90</td>\n",
       "      <td>8</td>\n",
       "      <td>7948.0</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
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       "      <td>1bd6fd288df4f0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1355.0</td>\n",
       "      <td>12</td>\n",
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       "      <td>...</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>96</td>\n",
       "      <td>10.40</td>\n",
       "      <td>1409.20</td>\n",
       "      <td>28.08</td>\n",
       "      <td>1473.68</td>\n",
       "      <td>8</td>\n",
       "      <td>3476.0</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Id         groupId         matchId  assists  boosts  \\\n",
       "281570  ab9d7168570927  add05ebde0214c  e016a873339c7b        2       3   \n",
       "346124  044d18fc42fc75  fc1dbc2df6a887  628107d4c41084        3       5   \n",
       "871244  e668a25f5488e3  5ba8feabfb2a23  f6e6581e03ba4f        0       4   \n",
       "908815  566d8218b705aa  a9b056478d71b2  3a41552d553583        2       5   \n",
       "963463  1bd6fd288df4f0  90584ffa22fe15  ba2de992ec7bb8        2       6   \n",
       "\n",
       "        damageDealt  DBNOs  headshotKills  heals  killPlace      ...        \\\n",
       "281570       1212.0      8             10      0          1      ...         \n",
       "346124       1620.0     13             11      3          1      ...         \n",
       "871244       1365.0      9             13      0          1      ...         \n",
       "908815       1535.0     10             10      3          1      ...         \n",
       "963463       1355.0     12             10      2          1      ...         \n",
       "\n",
       "        winPlacePerc  playersJoined  killsNorm  damageDealtNorm  maxPlaceNorm  \\\n",
       "281570        0.8462             93      10.70          1296.84         28.89   \n",
       "346124        1.0000             96      11.44          1684.80         28.08   \n",
       "871244        1.0000             98      13.26          1392.30         27.54   \n",
       "908815        0.9630             95      10.50          1611.75         29.40   \n",
       "963463        1.0000             96      10.40          1409.20         28.08   \n",
       "\n",
       "       matchDurationNorm  healsandboosts  totalDistance  killwithoutMoving  \\\n",
       "281570           1522.61               3         2939.0              False   \n",
       "346124           1796.08               8         8142.0              False   \n",
       "871244           1280.10               4         2105.0              False   \n",
       "908815           1929.90               8         7948.0              False   \n",
       "963463           1473.68               8         3476.0              False   \n",
       "\n",
       "        headshot_rate  \n",
       "281570            1.0  \n",
       "346124            1.0  \n",
       "871244            1.0  \n",
       "908815            1.0  \n",
       "963463            1.0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[(train[\"headshot_rate\"] == 1) & (train[\"kills\"] > 9)].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 281570,  346124,  871244,  908815,  963463, 1079403, 1167959,\n",
       "            1348164, 1380385, 1483199, 1581850, 1622232, 1753322, 2256755,\n",
       "            2375749, 2647056, 2825200, 3288424, 3594399, 3926325, 4036281,\n",
       "            4351048, 4387092, 4428741],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[(train[\"headshot_rate\"] == 1) & (train[\"kills\"] > 9)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4445307, 38)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(train[(train[\"headshot_rate\"] == 1) & (train[\"kills\"] > 9)].index, inplace=True)\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 异常值处理：删除最远杀敌距离异常数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>60</td>\n",
       "      <td>18.20</td>\n",
       "      <td>1323.560</td>\n",
       "      <td>11.20</td>\n",
       "      <td>1673.00</td>\n",
       "      <td>0</td>\n",
       "      <td>844.70</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2592718</th>\n",
       "      <td>24e0fec84c18e9</td>\n",
       "      <td>8404855ca02e48</td>\n",
       "      <td>e886a8ebb702cf</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1684.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5714</td>\n",
       "      <td>26</td>\n",
       "      <td>22.62</td>\n",
       "      <td>2930.160</td>\n",
       "      <td>38.28</td>\n",
       "      <td>3118.08</td>\n",
       "      <td>7</td>\n",
       "      <td>4851.00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.307692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2981715</th>\n",
       "      <td>7f77051c7cef52</td>\n",
       "      <td>d6579a630399b5</td>\n",
       "      <td>4784f7d9a06b51</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>93</td>\n",
       "      <td>6.42</td>\n",
       "      <td>1096.750</td>\n",
       "      <td>50.29</td>\n",
       "      <td>1453.06</td>\n",
       "      <td>10</td>\n",
       "      <td>4085.96</td>\n",
       "      <td>False</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3081503</th>\n",
       "      <td>f19a76e8d7ac52</td>\n",
       "      <td>624d65c529f87c</td>\n",
       "      <td>de19b70121c40f</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8571</td>\n",
       "      <td>57</td>\n",
       "      <td>8.58</td>\n",
       "      <td>1484.340</td>\n",
       "      <td>11.44</td>\n",
       "      <td>945.23</td>\n",
       "      <td>0</td>\n",
       "      <td>270.00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3255171</th>\n",
       "      <td>5524c154448425</td>\n",
       "      <td>674195558ad41b</td>\n",
       "      <td>db6f6d1f0d4904</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1355.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5789</td>\n",
       "      <td>21</td>\n",
       "      <td>25.06</td>\n",
       "      <td>2425.450</td>\n",
       "      <td>35.80</td>\n",
       "      <td>1607.42</td>\n",
       "      <td>0</td>\n",
       "      <td>1039.00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3304284</th>\n",
       "      <td>d0c286ce498e17</td>\n",
       "      <td>17fdd45e612bab</td>\n",
       "      <td>3eaaa2f7a360fe</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2330.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>53</td>\n",
       "      <td>29.40</td>\n",
       "      <td>3425.100</td>\n",
       "      <td>26.46</td>\n",
       "      <td>1321.53</td>\n",
       "      <td>0</td>\n",
       "      <td>68.02</td>\n",
       "      <td>False</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3320960</th>\n",
       "      <td>0040e53dfe7b5d</td>\n",
       "      <td>650661c2351eb7</td>\n",
       "      <td>2daabf3a7852e6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>399.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>15</td>\n",
       "      <td>7.40</td>\n",
       "      <td>738.150</td>\n",
       "      <td>14.80</td>\n",
       "      <td>2763.90</td>\n",
       "      <td>6</td>\n",
       "      <td>5481.00</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3552532</th>\n",
       "      <td>db638834c62f6f</td>\n",
       "      <td>0614b611d6a935</td>\n",
       "      <td>ff80300f8262f5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>517.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>30</td>\n",
       "      <td>8.50</td>\n",
       "      <td>878.900</td>\n",
       "      <td>6.80</td>\n",
       "      <td>612.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1344.88</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4332473</th>\n",
       "      <td>d8857d3d7e31b6</td>\n",
       "      <td>085de7a36897e6</td>\n",
       "      <td>42f997c16d8a0e</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1685.0</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9091</td>\n",
       "      <td>26</td>\n",
       "      <td>27.84</td>\n",
       "      <td>2931.900</td>\n",
       "      <td>20.88</td>\n",
       "      <td>3119.82</td>\n",
       "      <td>18</td>\n",
       "      <td>523.30</td>\n",
       "      <td>False</td>\n",
       "      <td>0.187500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Id         groupId         matchId  assists  boosts  \\\n",
       "202281   88e2af7d78af5a  34ddeede52c042  4346bc63bc67fa        0       3   \n",
       "240005   41c2f5c0699807  9faecf87ab4275  634edab75860b3        5       0   \n",
       "324313   ef390c152bcc3d  30fd444be3bbc1  4f7f8d6cf558b4        2       0   \n",
       "656553   9948b058562163  c8cb8491112bf6  0104eeb664494d        6       0   \n",
       "803632   4e7e6c74e3c57d  94698690918933  da91b0c3d875f8        0       0   \n",
       "895411   1f5ba6e0cfb968  512ea24b831be3  5fb0d8b1fc16cf        4       0   \n",
       "1172437  303a93cfa1f46c  8795d39fd0df86  9c8962b58bb3e3        2       1   \n",
       "1209416  528659ff1c1aec  7d1ba83423551d  ea9386587d5888        0       6   \n",
       "1642712  91966848e08e2f  0ee4fbd27657c9  17dea22cefe62a        3       2   \n",
       "2015559  5ff0c1a9fab2ba  2d8119b1544f87  904cecf36217df        3       3   \n",
       "2122128  42df3102cb540b  7d9b2be15b355b  610d78f3affd2e        5       0   \n",
       "2152425  4b9f61bac5eb0a  bc717b964f3bbe  838cb9a3c94598        3       0   \n",
       "2592718  24e0fec84c18e9  8404855ca02e48  e886a8ebb702cf        7       0   \n",
       "2981715  7f77051c7cef52  d6579a630399b5  4784f7d9a06b51        3       5   \n",
       "3081503  f19a76e8d7ac52  624d65c529f87c  de19b70121c40f        3       0   \n",
       "3255171  5524c154448425  674195558ad41b  db6f6d1f0d4904        1       0   \n",
       "3304284  d0c286ce498e17  17fdd45e612bab  3eaaa2f7a360fe        7       0   \n",
       "3320960  0040e53dfe7b5d  650661c2351eb7  2daabf3a7852e6        0       0   \n",
       "3552532  db638834c62f6f  0614b611d6a935  ff80300f8262f5        2       0   \n",
       "4332473  d8857d3d7e31b6  085de7a36897e6  42f997c16d8a0e        5       0   \n",
       "\n",
       "         damageDealt  DBNOs  headshotKills  heals  killPlace      ...        \\\n",
       "202281         783.9      5              1      1          5      ...         \n",
       "240005        1284.0      8              5      7         18      ...         \n",
       "324313        1028.0      0              0      0          9      ...         \n",
       "656553        1410.0     17              5      0          3      ...         \n",
       "803632         196.8      0              0      0         51      ...         \n",
       "895411        1012.0     11              5      0          5      ...         \n",
       "1172437        329.3      0              0      2         45      ...         \n",
       "1209416       1640.0      0              7      0          1      ...         \n",
       "1642712       2103.0      0              4     11         11      ...         \n",
       "2015559       1302.0      0              6      5         15      ...         \n",
       "2122128       2500.0      0              7      1          2      ...         \n",
       "2152425        945.4      0              0      0         11      ...         \n",
       "2592718       1684.0      0              4      7         11      ...         \n",
       "2981715       1025.0      5              2      5          2      ...         \n",
       "3081503       1038.0      0              0      0         32      ...         \n",
       "3255171       1355.0      0              2      0          9      ...         \n",
       "3304284       2330.0      0              2      0          2      ...         \n",
       "3320960        399.0      2              0      6         14      ...         \n",
       "3552532        517.0      0              0      0         10      ...         \n",
       "4332473       1685.0     11              3     18          8      ...         \n",
       "\n",
       "         winPlacePerc  playersJoined  killsNorm  damageDealtNorm  \\\n",
       "202281         0.9231             88       4.48          877.968   \n",
       "240005         0.5385             29      18.81         2195.640   \n",
       "324313         1.0000             51      14.90         1531.720   \n",
       "656553         0.6000             41      25.44         2241.900   \n",
       "803632         0.0000             61       1.39          273.552   \n",
       "895411         0.9091             86      11.40         1153.680   \n",
       "1172437        0.2857             58       4.26          467.606   \n",
       "1209416        0.9412             52      22.20         2427.200   \n",
       "1642712        0.5000             28      39.56         3617.160   \n",
       "2015559        0.6000             42      17.38         2057.160   \n",
       "2122128        0.0000             10      41.80         4750.000   \n",
       "2152425        0.5714             60      18.20         1323.560   \n",
       "2592718        0.5714             26      22.62         2930.160   \n",
       "2981715        1.0000             93       6.42         1096.750   \n",
       "3081503        0.8571             57       8.58         1484.340   \n",
       "3255171        0.5789             21      25.06         2425.450   \n",
       "3304284        1.0000             53      29.40         3425.100   \n",
       "3320960        0.0000             15       7.40          738.150   \n",
       "3552532        0.0000             30       8.50          878.900   \n",
       "4332473        0.9091             26      27.84         2931.900   \n",
       "\n",
       "         maxPlaceNorm matchDurationNorm  healsandboosts  totalDistance  \\\n",
       "202281          30.24           2087.68               4        3775.20   \n",
       "240005          23.94           2236.68               7          48.87   \n",
       "324313          19.37           1040.02               0        2981.00   \n",
       "656553           9.54           1734.69               0          29.21   \n",
       "803632          11.12            654.69               0        3159.00   \n",
       "895411          13.68           1163.94               0         569.50   \n",
       "1172437         11.36            825.02               3         832.50   \n",
       "1209416         76.96           1827.80               6        2848.00   \n",
       "1642712         25.80           3092.56              13         235.30   \n",
       "2015559         17.38           2834.52               8         133.20   \n",
       "2122128          3.80           3416.20               1         464.50   \n",
       "2152425         11.20           1673.00               0         844.70   \n",
       "2592718         38.28           3118.08               7        4851.00   \n",
       "2981715         50.29           1453.06              10        4085.96   \n",
       "3081503         11.44            945.23               0         270.00   \n",
       "3255171         35.80           1607.42               0        1039.00   \n",
       "3304284         26.46           1321.53               0          68.02   \n",
       "3320960         14.80           2763.90               6        5481.00   \n",
       "3552532          6.80            612.00               0        1344.88   \n",
       "4332473         20.88           3119.82              18         523.30   \n",
       "\n",
       "         killwithoutMoving  headshot_rate  \n",
       "202281               False       0.250000  \n",
       "240005               False       0.454545  \n",
       "324313               False       0.000000  \n",
       "656553               False       0.312500  \n",
       "803632               False       0.000000  \n",
       "895411               False       0.500000  \n",
       "1172437              False       0.000000  \n",
       "1209416              False       0.466667  \n",
       "1642712              False       0.173913  \n",
       "2015559              False       0.545455  \n",
       "2122128              False       0.318182  \n",
       "2152425              False       0.000000  \n",
       "2592718              False       0.307692  \n",
       "2981715              False       0.333333  \n",
       "3081503              False       0.000000  \n",
       "3255171              False       0.142857  \n",
       "3304284              False       0.100000  \n",
       "3320960              False       0.000000  \n",
       "3552532              False       0.000000  \n",
       "4332473              False       0.187500  \n",
       "\n",
       "[20 rows x 38 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train[\"longestKill\"] >=1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 202281,  240005,  324313,  656553,  803632,  895411, 1172437,\n",
       "            1209416, 1642712, 2015559, 2122128, 2152425, 2592718, 2981715,\n",
       "            3081503, 3255171, 3304284, 3320960, 3552532, 4332473],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train[\"longestKill\"] >=1000].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4445287, 38)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(train[train[\"longestKill\"] >=1000].index, inplace=True)\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 异常值处理：删除关于运动距离的异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  23026,   34344,   49312,   68590,   94400,  125103,  136421,\n",
       "             136476,  154080,  154128,\n",
       "            ...\n",
       "            4181311, 4230073, 4259976, 4284974, 4288445, 4306598, 4370543,\n",
       "            4380785, 4405009, 4415088],\n",
       "           dtype='int64', length=219)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 行走\n",
    "train[train[\"walkDistance\"] >=10000].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4445068, 38)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(train[train[\"walkDistance\"] >=10000].index, inplace=True)\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  28588,   63015,   70507,   72763,   95276,  140097,  297186,\n",
       "             371098,  403647,  426708,\n",
       "            ...\n",
       "            4154459, 4191491, 4239725, 4248221, 4256764, 4270943, 4301013,\n",
       "            4386384, 4404738, 4440261],\n",
       "           dtype='int64', length=150)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 载具\n",
    "train[train[\"rideDistance\"] >=20000].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4444918, 38)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(train[train[\"rideDistance\"] >=20000].index, inplace=True)\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 177973,  274258, 1005337, 1195818, 1227362, 1889163, 2065940,\n",
       "            2327586, 2784855, 3359439, 3513522, 4132225],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 游泳\n",
    "train[train[\"swimDistance\"] >=2000].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4444918, 38)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(train[train[\"swimDistance\"] >=20000].index, inplace=True)\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 异常值处理：武器收集异常值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 233643,  588387, 1437471, 1449293, 1592744, 1834515, 2373240,\n",
       "            2442962, 2743408, 2749693, 2797867, 2973445, 2977084, 2982525,\n",
       "            3230315, 3405716, 3951710, 4022031, 4288517],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train[\"weaponsAcquired\"] >=80].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4444899, 38)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(train[train[\"weaponsAcquired\"] >=80].index, inplace=True)\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 异常值处理：删除使用治疗药品数量异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([4262662], dtype='int64')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train[\"heals\"] >=80].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4444898, 38)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.drop(train[train[\"heals\"] >=80].index, inplace=True)\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 类别型数据处理\n",
    "#### 比赛类型one-hot处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['squad-fpp', 'duo', 'solo-fpp', 'squad', 'duo-fpp', 'solo',\n",
       "       'normal-squad-fpp', 'crashfpp', 'flaretpp', 'normal-solo-fpp',\n",
       "       'flarefpp', 'normal-duo-fpp', 'normal-duo', 'normal-squad',\n",
       "       'crashtpp', 'normal-solo'], dtype=object)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"matchType\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.get_dummies(train, columns=[\"matchType\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</th>\n",
       "      <th>assists</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>...</th>\n",
       "      <th>matchType_normal-duo</th>\n",
       "      <th>matchType_normal-duo-fpp</th>\n",
       "      <th>matchType_normal-solo</th>\n",
       "      <th>matchType_normal-solo-fpp</th>\n",
       "      <th>matchType_normal-squad</th>\n",
       "      <th>matchType_normal-squad-fpp</th>\n",
       "      <th>matchType_solo</th>\n",
       "      <th>matchType_solo-fpp</th>\n",
       "      <th>matchType_squad</th>\n",
       "      <th>matchType_squad-fpp</th>\n",
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       "      <td>...</td>\n",
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       "      <th>1</th>\n",
       "      <td>eef90569b9d03c</td>\n",
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       "      <td>aeb375fc57110c</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91.47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4616d365dd2853</td>\n",
       "      <td>a930a9c79cd721</td>\n",
       "      <td>f1f1f4ef412d7e</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>315c96c26c9aac</td>\n",
       "      <td>de04010b3458dd</td>\n",
       "      <td>6dc8ff871e21e6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id         groupId         matchId  assists  boosts  \\\n",
       "0  7f96b2f878858a  4d4b580de459be  a10357fd1a4a91        0       0   \n",
       "1  eef90569b9d03c  684d5656442f9e  aeb375fc57110c        0       0   \n",
       "2  1eaf90ac73de72  6a4a42c3245a74  110163d8bb94ae        1       0   \n",
       "3  4616d365dd2853  a930a9c79cd721  f1f1f4ef412d7e        0       0   \n",
       "4  315c96c26c9aac  de04010b3458dd  6dc8ff871e21e6        0       0   \n",
       "\n",
       "   damageDealt  DBNOs  headshotKills  heals  killPlace         ...           \\\n",
       "0         0.00      0              0      0         60         ...            \n",
       "1        91.47      0              0      0         57         ...            \n",
       "2        68.00      0              0      0         47         ...            \n",
       "3        32.90      0              0      0         75         ...            \n",
       "4       100.00      0              0      0         45         ...            \n",
       "\n",
       "   matchType_normal-duo  matchType_normal-duo-fpp  matchType_normal-solo  \\\n",
       "0                     0                         0                      0   \n",
       "1                     0                         0                      0   \n",
       "2                     0                         0                      0   \n",
       "3                     0                         0                      0   \n",
       "4                     0                         0                      0   \n",
       "\n",
       "   matchType_normal-solo-fpp  matchType_normal-squad  \\\n",
       "0                          0                       0   \n",
       "1                          0                       0   \n",
       "2                          0                       0   \n",
       "3                          0                       0   \n",
       "4                          0                       0   \n",
       "\n",
       "   matchType_normal-squad-fpp  matchType_solo  matchType_solo-fpp  \\\n",
       "0                           0               0                   0   \n",
       "1                           0               0                   0   \n",
       "2                           0               0                   0   \n",
       "3                           0               0                   0   \n",
       "4                           0               0                   1   \n",
       "\n",
       "   matchType_squad  matchType_squad-fpp  \n",
       "0                0                    1  \n",
       "1                0                    1  \n",
       "2                0                    0  \n",
       "3                0                    1  \n",
       "4                0                    0  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "matchType_encoding = train.filter(regex=\"matchType\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>matchType_crashfpp</th>\n",
       "      <th>matchType_crashtpp</th>\n",
       "      <th>matchType_duo</th>\n",
       "      <th>matchType_duo-fpp</th>\n",
       "      <th>matchType_flarefpp</th>\n",
       "      <th>matchType_flaretpp</th>\n",
       "      <th>matchType_normal-duo</th>\n",
       "      <th>matchType_normal-duo-fpp</th>\n",
       "      <th>matchType_normal-solo</th>\n",
       "      <th>matchType_normal-solo-fpp</th>\n",
       "      <th>matchType_normal-squad</th>\n",
       "      <th>matchType_normal-squad-fpp</th>\n",
       "      <th>matchType_solo</th>\n",
       "      <th>matchType_solo-fpp</th>\n",
       "      <th>matchType_squad</th>\n",
       "      <th>matchType_squad-fpp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   matchType_crashfpp  matchType_crashtpp  matchType_duo  matchType_duo-fpp  \\\n",
       "0                   0                   0              0                  0   \n",
       "1                   0                   0              0                  0   \n",
       "2                   0                   0              1                  0   \n",
       "3                   0                   0              0                  0   \n",
       "4                   0                   0              0                  0   \n",
       "\n",
       "   matchType_flarefpp  matchType_flaretpp  matchType_normal-duo  \\\n",
       "0                   0                   0                     0   \n",
       "1                   0                   0                     0   \n",
       "2                   0                   0                     0   \n",
       "3                   0                   0                     0   \n",
       "4                   0                   0                     0   \n",
       "\n",
       "   matchType_normal-duo-fpp  matchType_normal-solo  matchType_normal-solo-fpp  \\\n",
       "0                         0                      0                          0   \n",
       "1                         0                      0                          0   \n",
       "2                         0                      0                          0   \n",
       "3                         0                      0                          0   \n",
       "4                         0                      0                          0   \n",
       "\n",
       "   matchType_normal-squad  matchType_normal-squad-fpp  matchType_solo  \\\n",
       "0                       0                           0               0   \n",
       "1                       0                           0               0   \n",
       "2                       0                           0               0   \n",
       "3                       0                           0               0   \n",
       "4                       0                           0               0   \n",
       "\n",
       "   matchType_solo-fpp  matchType_squad  matchType_squad-fpp  \n",
       "0                   0                0                    1  \n",
       "1                   0                0                    1  \n",
       "2                   0                0                    0  \n",
       "3                   0                0                    1  \n",
       "4                   1                0                    0  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matchType_encoding.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 对groupId,matchId等数据进行处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4d4b580de459be\n",
       "1    684d5656442f9e\n",
       "2    6a4a42c3245a74\n",
       "3    a930a9c79cd721\n",
       "4    de04010b3458dd\n",
       "Name: groupId, dtype: object"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"groupId\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train[\"groupId\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"groupId\"] = train[\"groupId\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[\"groupId_cat\"] = train[\"groupId\"].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     613619\n",
       "1     827616\n",
       "2     843307\n",
       "3    1340122\n",
       "4    1757411\n",
       "Name: groupId_cat, dtype: int32"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"groupId_cat\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    30085\n",
       "1    32751\n",
       "2     3143\n",
       "3    45260\n",
       "4    20531\n",
       "Name: matchId_cat, dtype: int32"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[\"matchId\"] = train[\"matchId\"].astype(\"category\")\n",
    "train[\"matchId_cat\"] = train[\"matchId\"].cat.codes\n",
    "train[\"matchId_cat\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>groupId</th>\n",
       "      <th>matchId</th>\n",
       "      <th>assists</th>\n",
       "      <th>boosts</th>\n",
       "      <th>damageDealt</th>\n",
       "      <th>DBNOs</th>\n",
       "      <th>headshotKills</th>\n",
       "      <th>heals</th>\n",
       "      <th>killPlace</th>\n",
       "      <th>...</th>\n",
       "      <th>matchType_normal-solo</th>\n",
       "      <th>matchType_normal-solo-fpp</th>\n",
       "      <th>matchType_normal-squad</th>\n",
       "      <th>matchType_normal-squad-fpp</th>\n",
       "      <th>matchType_solo</th>\n",
       "      <th>matchType_solo-fpp</th>\n",
       "      <th>matchType_squad</th>\n",
       "      <th>matchType_squad-fpp</th>\n",
       "      <th>groupId_cat</th>\n",
       "      <th>matchId_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7f96b2f878858a</td>\n",
       "      <td>4d4b580de459be</td>\n",
       "      <td>a10357fd1a4a91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>613619</td>\n",
       "      <td>30085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eef90569b9d03c</td>\n",
       "      <td>684d5656442f9e</td>\n",
       "      <td>aeb375fc57110c</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91.47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>827616</td>\n",
       "      <td>32751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1eaf90ac73de72</td>\n",
       "      <td>6a4a42c3245a74</td>\n",
       "      <td>110163d8bb94ae</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>68.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>843307</td>\n",
       "      <td>3143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4616d365dd2853</td>\n",
       "      <td>a930a9c79cd721</td>\n",
       "      <td>f1f1f4ef412d7e</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.90</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1340122</td>\n",
       "      <td>45260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>315c96c26c9aac</td>\n",
       "      <td>de04010b3458dd</td>\n",
       "      <td>6dc8ff871e21e6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1757411</td>\n",
       "      <td>20531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id         groupId         matchId  assists  boosts  \\\n",
       "0  7f96b2f878858a  4d4b580de459be  a10357fd1a4a91        0       0   \n",
       "1  eef90569b9d03c  684d5656442f9e  aeb375fc57110c        0       0   \n",
       "2  1eaf90ac73de72  6a4a42c3245a74  110163d8bb94ae        1       0   \n",
       "3  4616d365dd2853  a930a9c79cd721  f1f1f4ef412d7e        0       0   \n",
       "4  315c96c26c9aac  de04010b3458dd  6dc8ff871e21e6        0       0   \n",
       "\n",
       "   damageDealt  DBNOs  headshotKills  heals  killPlace     ...       \\\n",
       "0         0.00      0              0      0         60     ...        \n",
       "1        91.47      0              0      0         57     ...        \n",
       "2        68.00      0              0      0         47     ...        \n",
       "3        32.90      0              0      0         75     ...        \n",
       "4       100.00      0              0      0         45     ...        \n",
       "\n",
       "   matchType_normal-solo  matchType_normal-solo-fpp  matchType_normal-squad  \\\n",
       "0                      0                          0                       0   \n",
       "1                      0                          0                       0   \n",
       "2                      0                          0                       0   \n",
       "3                      0                          0                       0   \n",
       "4                      0                          0                       0   \n",
       "\n",
       "   matchType_normal-squad-fpp  matchType_solo  matchType_solo-fpp  \\\n",
       "0                           0               0                   0   \n",
       "1                           0               0                   0   \n",
       "2                           0               0                   0   \n",
       "3                           0               0                   0   \n",
       "4                           0               0                   1   \n",
       "\n",
       "   matchType_squad  matchType_squad-fpp  groupId_cat  matchId_cat  \n",
       "0                0                    1       613619        30085  \n",
       "1                0                    1       827616        32751  \n",
       "2                0                    0       843307         3143  \n",
       "3                0                    1      1340122        45260  \n",
       "4                0                    0      1757411        20531  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop([\"groupId\", \"matchId\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据截取\n",
    "#### 取部分数据进行使用（100000）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sample = train.sample(100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 53)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 确定特征值和目标值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_sample.drop([\"winPlacePerc\", \"Id\"], axis=1)\n",
    "\n",
    "y = df_sample[\"winPlacePerc\"]                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 51)"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000,)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分割训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(df, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80000, 51)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80000,)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 机器学习（模型训练）和评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用随机森林对模型进行训练\n",
    "#### 初步使用随机森林进行模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=3,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=40, n_jobs=-1, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1 = RandomForestRegressor(n_estimators=40, \n",
    "                           min_samples_leaf=3, \n",
    "                           max_features='sqrt',\n",
    "                           n_jobs=-1)\n",
    "\n",
    "m1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.907159951456783"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pre = m1.predict(X_valid)\n",
    "m1.score(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06647584387091089"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_valid, y_pre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 再次使用随机森林，进行模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.87658119e-03, 8.63746064e-02, 2.57685962e-02, 2.27532062e-03,\n",
       "       8.80290888e-04, 2.81013428e-02, 2.35573150e-01, 2.07083462e-03,\n",
       "       1.22714874e-02, 1.09702183e-02, 2.53353780e-02, 1.03767737e-02,\n",
       "       6.77020063e-03, 7.45172312e-03, 4.28638708e-03, 3.28563034e-03,\n",
       "       2.12925123e-02, 1.99967979e-05, 3.98400033e-03, 1.36372746e-04,\n",
       "       1.32592980e-04, 1.71940999e-01, 4.20790087e-02, 2.52040557e-03,\n",
       "       6.33213818e-03, 7.30402941e-03, 1.11477263e-02, 7.52171932e-03,\n",
       "       1.19465432e-02, 5.30382552e-02, 1.81348675e-01, 0.00000000e+00,\n",
       "       2.22747761e-03, 3.44612223e-05, 0.00000000e+00, 2.02507233e-04,\n",
       "       5.97533128e-04, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       3.54286394e-05, 0.00000000e+00, 4.94182811e-07, 0.00000000e+00,\n",
       "       3.11300574e-04, 1.93840925e-04, 9.02659739e-04, 1.04955041e-03,\n",
       "       9.87217660e-04, 4.53628891e-03, 4.50774311e-03])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_df = pd.DataFrame({\"cols\":df.columns, \"imp\":m1.feature_importances_})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cols</th>\n",
       "      <th>imp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>assists</td>\n",
       "      <td>0.001877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>boosts</td>\n",
       "      <td>0.086375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>damageDealt</td>\n",
       "      <td>0.025769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DBNOs</td>\n",
       "      <td>0.002275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>headshotKills</td>\n",
       "      <td>0.000880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cols       imp\n",
       "0        assists  0.001877\n",
       "1         boosts  0.086375\n",
       "2    damageDealt  0.025769\n",
       "3          DBNOs  0.002275\n",
       "4  headshotKills  0.000880"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_df = imp_df.sort_values(\"imp\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cols</th>\n",
       "      <th>imp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>killPlace</td>\n",
       "      <td>0.235573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>totalDistance</td>\n",
       "      <td>0.181349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>walkDistance</td>\n",
       "      <td>0.171941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>boosts</td>\n",
       "      <td>0.086375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>healsandboosts</td>\n",
       "      <td>0.053038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              cols       imp\n",
       "6        killPlace  0.235573\n",
       "30   totalDistance  0.181349\n",
       "21    walkDistance  0.171941\n",
       "1           boosts  0.086375\n",
       "29  healsandboosts  0.053038"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1500a080>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp_df[:20].plot(\"cols\", \"imp\", figsize=(20, 8), kind=\"barh\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_keep = imp_df[imp_df.imp > 0.005].cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20,)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_keep.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_keep = df[to_keep]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(df_keep, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80000, 20)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
       "                      max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                      max_samples=None, min_impurity_decrease=0.0,\n",
       "                      min_impurity_split=None, min_samples_leaf=3,\n",
       "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                      n_estimators=40, n_jobs=-1, oob_score=False,\n",
       "                      random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2 = RandomForestRegressor(n_estimators=40, \n",
    "                           min_samples_leaf=3, \n",
    "                           max_features='sqrt',\n",
    "                           n_jobs=-1)\n",
    "\n",
    "m2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9125654968172906"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pre = m2.predict(X_valid)\n",
    "m2.score(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06408683094647326"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_valid, y_pre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用lightGBM对模型进行训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(df, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80000, 51)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型初次尝试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's l1: 0.255801\tvalid_0's l2: 0.0863836\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[2]\tvalid_0's l1: 0.244604\tvalid_0's l2: 0.0792314\n",
      "[3]\tvalid_0's l1: 0.234038\tvalid_0's l2: 0.072761\n",
      "[4]\tvalid_0's l1: 0.224123\tvalid_0's l2: 0.0669453\n",
      "[5]\tvalid_0's l1: 0.214716\tvalid_0's l2: 0.0616647\n",
      "[6]\tvalid_0's l1: 0.205802\tvalid_0's l2: 0.0568409\n",
      "[7]\tvalid_0's l1: 0.197424\tvalid_0's l2: 0.0525102\n",
      "[8]\tvalid_0's l1: 0.189497\tvalid_0's l2: 0.0485595\n",
      "[9]\tvalid_0's l1: 0.18208\tvalid_0's l2: 0.0450087\n",
      "[10]\tvalid_0's l1: 0.175038\tvalid_0's l2: 0.0417809\n",
      "[11]\tvalid_0's l1: 0.168411\tvalid_0's l2: 0.0388494\n",
      "[12]\tvalid_0's l1: 0.162014\tvalid_0's l2: 0.0361473\n",
      "[13]\tvalid_0's l1: 0.156139\tvalid_0's l2: 0.0337388\n",
      "[14]\tvalid_0's l1: 0.150548\tvalid_0's l2: 0.031546\n",
      "[15]\tvalid_0's l1: 0.145259\tvalid_0's l2: 0.0295381\n",
      "[16]\tvalid_0's l1: 0.140261\tvalid_0's l2: 0.0277049\n",
      "[17]\tvalid_0's l1: 0.135596\tvalid_0's l2: 0.0260668\n",
      "[18]\tvalid_0's l1: 0.131269\tvalid_0's l2: 0.0245903\n",
      "[19]\tvalid_0's l1: 0.127159\tvalid_0's l2: 0.0232428\n",
      "[20]\tvalid_0's l1: 0.123315\tvalid_0's l2: 0.0220185\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[20]\tvalid_0's l1: 0.123315\tvalid_0's l2: 0.0220185\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "              importance_type='split', learning_rate=0.05, max_depth=-1,\n",
       "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
       "              n_estimators=20, n_jobs=-1, num_leaves=31, objective='regression',\n",
       "              random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
       "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm = lgb.LGBMRegressor(objective=\"regression\", num_leaves=31, learning_rate=0.05, n_estimators=20)\n",
    "\n",
    "gbm.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], eval_metric=\"l1\", early_stopping_rounds=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pre = gbm.predict(X_valid, num_iteration=gbm.best_iteration_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12331524150224461"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(y_valid, y_pre)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型二次调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=LGBMRegressor(boosting_type='gbdt', class_weight=None,\n",
       "                                     colsample_bytree=1.0,\n",
       "                                     importance_type='split', learning_rate=0.1,\n",
       "                                     max_depth=-1, min_child_samples=20,\n",
       "                                     min_child_weight=0.001, min_split_gain=0.0,\n",
       "                                     n_estimators=100, n_jobs=-1, num_leaves=31,\n",
       "                                     objective=None, random_state=None,\n",
       "                                     reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
       "                                     subsample=1.0, subsample_for_bin=200000,\n",
       "                                     subsample_freq=0),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid={'learning_rate': [0.01, 0.1, 1],\n",
       "                         'n_estimators': [40, 60, 80, 100, 200, 300]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator = lgb.LGBMRegressor(num_leaves=31)\n",
    "param_grid = {\n",
    "    \"learning_rate\":[0.01, 0.1, 1],\n",
    "    \"n_estimators\":[40, 60, 80, 100, 200, 300]\n",
    "}\n",
    "\n",
    "gbm = GridSearchCV(estimator, param_grid, cv=5, n_jobs=-1)\n",
    "\n",
    "gbm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05685004010605751"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pre = gbm.predict(X_valid)\n",
    "mean_absolute_error(y_valid, y_pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 0.1, 'n_estimators': 300}"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbm.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 模型三次调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's l1: 0.244506\tvalid_0's l2: 0.0790859\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[2]\tvalid_0's l1: 0.223857\tvalid_0's l2: 0.0667341\n",
      "[3]\tvalid_0's l1: 0.205626\tvalid_0's l2: 0.0566941\n",
      "[4]\tvalid_0's l1: 0.189298\tvalid_0's l2: 0.0484539\n",
      "[5]\tvalid_0's l1: 0.175104\tvalid_0's l2: 0.0417921\n",
      "[6]\tvalid_0's l1: 0.16227\tvalid_0's l2: 0.036285\n",
      "[7]\tvalid_0's l1: 0.150898\tvalid_0's l2: 0.0317221\n",
      "[8]\tvalid_0's l1: 0.140883\tvalid_0's l2: 0.0280196\n",
      "[9]\tvalid_0's l1: 0.131939\tvalid_0's l2: 0.0249613\n",
      "[10]\tvalid_0's l1: 0.124375\tvalid_0's l2: 0.0224944\n",
      "[11]\tvalid_0's l1: 0.117467\tvalid_0's l2: 0.0204263\n",
      "[12]\tvalid_0's l1: 0.110957\tvalid_0's l2: 0.0185453\n",
      "[13]\tvalid_0's l1: 0.105077\tvalid_0's l2: 0.016929\n",
      "[14]\tvalid_0's l1: 0.100272\tvalid_0's l2: 0.0157152\n",
      "[15]\tvalid_0's l1: 0.0960833\tvalid_0's l2: 0.0147431\n",
      "[16]\tvalid_0's l1: 0.0920567\tvalid_0's l2: 0.0137979\n",
      "[17]\tvalid_0's l1: 0.088268\tvalid_0's l2: 0.0129121\n",
      "[18]\tvalid_0's l1: 0.0854499\tvalid_0's l2: 0.0123437\n",
      "[19]\tvalid_0's l1: 0.0827065\tvalid_0's l2: 0.0117755\n",
      "[20]\tvalid_0's l1: 0.0800967\tvalid_0's l2: 0.0112162\n",
      "[21]\tvalid_0's l1: 0.0781237\tvalid_0's l2: 0.0108399\n",
      "[22]\tvalid_0's l1: 0.0763776\tvalid_0's l2: 0.0105204\n",
      "[23]\tvalid_0's l1: 0.074906\tvalid_0's l2: 0.010265\n",
      "[24]\tvalid_0's l1: 0.0732985\tvalid_0's l2: 0.00994854\n",
      "[25]\tvalid_0's l1: 0.0720481\tvalid_0's l2: 0.00970346\n",
      "[26]\tvalid_0's l1: 0.0710837\tvalid_0's l2: 0.00953733\n",
      "[27]\tvalid_0's l1: 0.0703066\tvalid_0's l2: 0.00939487\n",
      "[28]\tvalid_0's l1: 0.0694075\tvalid_0's l2: 0.009231\n",
      "[29]\tvalid_0's l1: 0.0685866\tvalid_0's l2: 0.00907982\n",
      "[30]\tvalid_0's l1: 0.06779\tvalid_0's l2: 0.0089391\n",
      "[31]\tvalid_0's l1: 0.067074\tvalid_0's l2: 0.00880224\n",
      "[32]\tvalid_0's l1: 0.0665149\tvalid_0's l2: 0.00871073\n",
      "[33]\tvalid_0's l1: 0.0659205\tvalid_0's l2: 0.00858992\n",
      "[34]\tvalid_0's l1: 0.0652426\tvalid_0's l2: 0.00843778\n",
      "[35]\tvalid_0's l1: 0.0648768\tvalid_0's l2: 0.00836372\n",
      "[36]\tvalid_0's l1: 0.0645306\tvalid_0's l2: 0.00830614\n",
      "[37]\tvalid_0's l1: 0.0640175\tvalid_0's l2: 0.00818645\n",
      "[38]\tvalid_0's l1: 0.0637947\tvalid_0's l2: 0.00814636\n",
      "[39]\tvalid_0's l1: 0.0635605\tvalid_0's l2: 0.00810447\n",
      "[40]\tvalid_0's l1: 0.0633449\tvalid_0's l2: 0.00806796\n",
      "[41]\tvalid_0's l1: 0.0631446\tvalid_0's l2: 0.00802436\n",
      "[42]\tvalid_0's l1: 0.0627575\tvalid_0's l2: 0.00792715\n",
      "[43]\tvalid_0's l1: 0.062611\tvalid_0's l2: 0.00789809\n",
      "[44]\tvalid_0's l1: 0.0623997\tvalid_0's l2: 0.00785422\n",
      "[45]\tvalid_0's l1: 0.0622728\tvalid_0's l2: 0.00783369\n",
      "[46]\tvalid_0's l1: 0.0621336\tvalid_0's l2: 0.00781042\n",
      "[47]\tvalid_0's l1: 0.0619906\tvalid_0's l2: 0.00777463\n",
      "[48]\tvalid_0's l1: 0.0618556\tvalid_0's l2: 0.00774471\n",
      "[49]\tvalid_0's l1: 0.0617449\tvalid_0's l2: 0.00772713\n",
      "[50]\tvalid_0's l1: 0.0616454\tvalid_0's l2: 0.00770345\n",
      "[51]\tvalid_0's l1: 0.0615492\tvalid_0's l2: 0.00768433\n",
      "[52]\tvalid_0's l1: 0.0614452\tvalid_0's l2: 0.00766572\n",
      "[53]\tvalid_0's l1: 0.0613725\tvalid_0's l2: 0.00764408\n",
      "[54]\tvalid_0's l1: 0.0612796\tvalid_0's l2: 0.00762834\n",
      "[55]\tvalid_0's l1: 0.0611706\tvalid_0's l2: 0.00760066\n",
      "[56]\tvalid_0's l1: 0.0611121\tvalid_0's l2: 0.00759012\n",
      "[57]\tvalid_0's l1: 0.0610472\tvalid_0's l2: 0.00757864\n",
      "[58]\tvalid_0's l1: 0.0609801\tvalid_0's l2: 0.00756664\n",
      "[59]\tvalid_0's l1: 0.0608993\tvalid_0's l2: 0.00755207\n",
      "[60]\tvalid_0's l1: 0.0608206\tvalid_0's l2: 0.00753343\n",
      "[61]\tvalid_0's l1: 0.0607751\tvalid_0's l2: 0.00752435\n",
      "[62]\tvalid_0's l1: 0.0607163\tvalid_0's l2: 0.00751128\n",
      "[63]\tvalid_0's l1: 0.0605746\tvalid_0's l2: 0.00747752\n",
      "[64]\tvalid_0's l1: 0.0604651\tvalid_0's l2: 0.0074507\n",
      "[65]\tvalid_0's l1: 0.0604263\tvalid_0's l2: 0.00744162\n",
      "[66]\tvalid_0's l1: 0.0603599\tvalid_0's l2: 0.00742889\n",
      "[67]\tvalid_0's l1: 0.0602701\tvalid_0's l2: 0.00741169\n",
      "[68]\tvalid_0's l1: 0.0602334\tvalid_0's l2: 0.00739977\n",
      "[69]\tvalid_0's l1: 0.060112\tvalid_0's l2: 0.007374\n",
      "[70]\tvalid_0's l1: 0.0600257\tvalid_0's l2: 0.00735234\n",
      "[71]\tvalid_0's l1: 0.059928\tvalid_0's l2: 0.00732775\n",
      "[72]\tvalid_0's l1: 0.0598568\tvalid_0's l2: 0.00731406\n",
      "[73]\tvalid_0's l1: 0.0598019\tvalid_0's l2: 0.007307\n",
      "[74]\tvalid_0's l1: 0.0597175\tvalid_0's l2: 0.00728248\n",
      "[75]\tvalid_0's l1: 0.0596246\tvalid_0's l2: 0.00726385\n",
      "[76]\tvalid_0's l1: 0.0596029\tvalid_0's l2: 0.00725546\n",
      "[77]\tvalid_0's l1: 0.0595231\tvalid_0's l2: 0.00723235\n",
      "[78]\tvalid_0's l1: 0.0595014\tvalid_0's l2: 0.00722831\n",
      "[79]\tvalid_0's l1: 0.0594712\tvalid_0's l2: 0.00722155\n",
      "[80]\tvalid_0's l1: 0.0594512\tvalid_0's l2: 0.00721585\n",
      "[81]\tvalid_0's l1: 0.0594197\tvalid_0's l2: 0.00720856\n",
      "[82]\tvalid_0's l1: 0.0593932\tvalid_0's l2: 0.00720475\n",
      "[83]\tvalid_0's l1: 0.0593157\tvalid_0's l2: 0.00718559\n",
      "[84]\tvalid_0's l1: 0.0592926\tvalid_0's l2: 0.00718174\n",
      "[85]\tvalid_0's l1: 0.0592627\tvalid_0's l2: 0.00717558\n",
      "[86]\tvalid_0's l1: 0.0592466\tvalid_0's l2: 0.00716984\n",
      "[87]\tvalid_0's l1: 0.0592043\tvalid_0's l2: 0.00716193\n",
      "[88]\tvalid_0's l1: 0.0591298\tvalid_0's l2: 0.00714342\n",
      "[89]\tvalid_0's l1: 0.059097\tvalid_0's l2: 0.00713607\n",
      "[90]\tvalid_0's l1: 0.0589982\tvalid_0's l2: 0.00711745\n",
      "[91]\tvalid_0's l1: 0.0589505\tvalid_0's l2: 0.00710413\n",
      "[92]\tvalid_0's l1: 0.0588884\tvalid_0's l2: 0.00708708\n",
      "[93]\tvalid_0's l1: 0.0588084\tvalid_0's l2: 0.00707062\n",
      "[94]\tvalid_0's l1: 0.0587967\tvalid_0's l2: 0.00707269\n",
      "[95]\tvalid_0's l1: 0.0587689\tvalid_0's l2: 0.00706628\n",
      "[96]\tvalid_0's l1: 0.058749\tvalid_0's l2: 0.00705746\n",
      "[97]\tvalid_0's l1: 0.0587121\tvalid_0's l2: 0.00704977\n",
      "[98]\tvalid_0's l1: 0.0586712\tvalid_0's l2: 0.00703874\n",
      "[99]\tvalid_0's l1: 0.05861\tvalid_0's l2: 0.0070237\n",
      "[100]\tvalid_0's l1: 0.0585889\tvalid_0's l2: 0.00701449\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[100]\tvalid_0's l1: 0.0585889\tvalid_0's l2: 0.00701449\n",
      "本次结果输出的mae值是:\n",
      " 0.05858885784299778\n",
      "[1]\tvalid_0's l1: 0.244506\tvalid_0's l2: 0.0790859\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[2]\tvalid_0's l1: 0.223857\tvalid_0's l2: 0.0667341\n",
      "[3]\tvalid_0's l1: 0.205626\tvalid_0's l2: 0.0566941\n",
      "[4]\tvalid_0's l1: 0.189298\tvalid_0's l2: 0.0484539\n",
      "[5]\tvalid_0's l1: 0.175104\tvalid_0's l2: 0.0417921\n",
      "[6]\tvalid_0's l1: 0.16227\tvalid_0's l2: 0.036285\n",
      "[7]\tvalid_0's l1: 0.150898\tvalid_0's l2: 0.0317221\n",
      "[8]\tvalid_0's l1: 0.140883\tvalid_0's l2: 0.0280196\n",
      "[9]\tvalid_0's l1: 0.131939\tvalid_0's l2: 0.0249613\n",
      "[10]\tvalid_0's l1: 0.124375\tvalid_0's l2: 0.0224944\n",
      "[11]\tvalid_0's l1: 0.117467\tvalid_0's l2: 0.0204263\n",
      "[12]\tvalid_0's l1: 0.110957\tvalid_0's l2: 0.0185453\n",
      "[13]\tvalid_0's l1: 0.105077\tvalid_0's l2: 0.016929\n",
      "[14]\tvalid_0's l1: 0.100272\tvalid_0's l2: 0.0157152\n",
      "[15]\tvalid_0's l1: 0.0960833\tvalid_0's l2: 0.0147431\n",
      "[16]\tvalid_0's l1: 0.0920567\tvalid_0's l2: 0.0137979\n",
      "[17]\tvalid_0's l1: 0.088268\tvalid_0's l2: 0.0129121\n",
      "[18]\tvalid_0's l1: 0.0854499\tvalid_0's l2: 0.0123437\n",
      "[19]\tvalid_0's l1: 0.0827065\tvalid_0's l2: 0.0117755\n",
      "[20]\tvalid_0's l1: 0.0800967\tvalid_0's l2: 0.0112162\n",
      "[21]\tvalid_0's l1: 0.0781237\tvalid_0's l2: 0.0108399\n",
      "[22]\tvalid_0's l1: 0.0763776\tvalid_0's l2: 0.0105204\n",
      "[23]\tvalid_0's l1: 0.074906\tvalid_0's l2: 0.010265\n",
      "[24]\tvalid_0's l1: 0.0732985\tvalid_0's l2: 0.00994854\n",
      "[25]\tvalid_0's l1: 0.0720481\tvalid_0's l2: 0.00970346\n",
      "[26]\tvalid_0's l1: 0.0710837\tvalid_0's l2: 0.00953733\n",
      "[27]\tvalid_0's l1: 0.0703066\tvalid_0's l2: 0.00939487\n",
      "[28]\tvalid_0's l1: 0.0694075\tvalid_0's l2: 0.009231\n",
      "[29]\tvalid_0's l1: 0.0685866\tvalid_0's l2: 0.00907982\n",
      "[30]\tvalid_0's l1: 0.06779\tvalid_0's l2: 0.0089391\n",
      "[31]\tvalid_0's l1: 0.067074\tvalid_0's l2: 0.00880224\n",
      "[32]\tvalid_0's l1: 0.0665149\tvalid_0's l2: 0.00871073\n",
      "[33]\tvalid_0's l1: 0.0659205\tvalid_0's l2: 0.00858992\n",
      "[34]\tvalid_0's l1: 0.0652426\tvalid_0's l2: 0.00843778\n",
      "[35]\tvalid_0's l1: 0.0648768\tvalid_0's l2: 0.00836372\n",
      "[36]\tvalid_0's l1: 0.0645306\tvalid_0's l2: 0.00830614\n",
      "[37]\tvalid_0's l1: 0.0640175\tvalid_0's l2: 0.00818645\n",
      "[38]\tvalid_0's l1: 0.0637947\tvalid_0's l2: 0.00814636\n",
      "[39]\tvalid_0's l1: 0.0635605\tvalid_0's l2: 0.00810447\n",
      "[40]\tvalid_0's l1: 0.0633449\tvalid_0's l2: 0.00806796\n",
      "[41]\tvalid_0's l1: 0.0631446\tvalid_0's l2: 0.00802436\n",
      "[42]\tvalid_0's l1: 0.0627575\tvalid_0's l2: 0.00792715\n",
      "[43]\tvalid_0's l1: 0.062611\tvalid_0's l2: 0.00789809\n",
      "[44]\tvalid_0's l1: 0.0623997\tvalid_0's l2: 0.00785422\n",
      "[45]\tvalid_0's l1: 0.0622728\tvalid_0's l2: 0.00783369\n",
      "[46]\tvalid_0's l1: 0.0621336\tvalid_0's l2: 0.00781042\n",
      "[47]\tvalid_0's l1: 0.0619906\tvalid_0's l2: 0.00777463\n",
      "[48]\tvalid_0's l1: 0.0618556\tvalid_0's l2: 0.00774471\n",
      "[49]\tvalid_0's l1: 0.0617449\tvalid_0's l2: 0.00772713\n",
      "[50]\tvalid_0's l1: 0.0616454\tvalid_0's l2: 0.00770345\n",
      "[51]\tvalid_0's l1: 0.0615492\tvalid_0's l2: 0.00768433\n",
      "[52]\tvalid_0's l1: 0.0614452\tvalid_0's l2: 0.00766572\n",
      "[53]\tvalid_0's l1: 0.0613725\tvalid_0's l2: 0.00764408\n",
      "[54]\tvalid_0's l1: 0.0612796\tvalid_0's l2: 0.00762834\n",
      "[55]\tvalid_0's l1: 0.0611706\tvalid_0's l2: 0.00760066\n",
      "[56]\tvalid_0's l1: 0.0611121\tvalid_0's l2: 0.00759012\n",
      "[57]\tvalid_0's l1: 0.0610472\tvalid_0's l2: 0.00757864\n",
      "[58]\tvalid_0's l1: 0.0609801\tvalid_0's l2: 0.00756664\n",
      "[59]\tvalid_0's l1: 0.0608993\tvalid_0's l2: 0.00755207\n",
      "[60]\tvalid_0's l1: 0.0608206\tvalid_0's l2: 0.00753343\n",
      "[61]\tvalid_0's l1: 0.0607751\tvalid_0's l2: 0.00752435\n",
      "[62]\tvalid_0's l1: 0.0607163\tvalid_0's l2: 0.00751128\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[63]\tvalid_0's l1: 0.0605746\tvalid_0's l2: 0.00747752\n",
      "[64]\tvalid_0's l1: 0.0604651\tvalid_0's l2: 0.0074507\n",
      "[65]\tvalid_0's l1: 0.0604263\tvalid_0's l2: 0.00744162\n",
      "[66]\tvalid_0's l1: 0.0603599\tvalid_0's l2: 0.00742889\n",
      "[67]\tvalid_0's l1: 0.0602701\tvalid_0's l2: 0.00741169\n",
      "[68]\tvalid_0's l1: 0.0602334\tvalid_0's l2: 0.00739977\n",
      "[69]\tvalid_0's l1: 0.060112\tvalid_0's l2: 0.007374\n",
      "[70]\tvalid_0's l1: 0.0600257\tvalid_0's l2: 0.00735234\n",
      "[71]\tvalid_0's l1: 0.059928\tvalid_0's l2: 0.00732775\n",
      "[72]\tvalid_0's l1: 0.0598568\tvalid_0's l2: 0.00731406\n",
      "[73]\tvalid_0's l1: 0.0598019\tvalid_0's l2: 0.007307\n",
      "[74]\tvalid_0's l1: 0.0597175\tvalid_0's l2: 0.00728248\n",
      "[75]\tvalid_0's l1: 0.0596246\tvalid_0's l2: 0.00726385\n",
      "[76]\tvalid_0's l1: 0.0596029\tvalid_0's l2: 0.00725546\n",
      "[77]\tvalid_0's l1: 0.0595231\tvalid_0's l2: 0.00723235\n",
      "[78]\tvalid_0's l1: 0.0595014\tvalid_0's l2: 0.00722831\n",
      "[79]\tvalid_0's l1: 0.0594712\tvalid_0's l2: 0.00722155\n",
      "[80]\tvalid_0's l1: 0.0594512\tvalid_0's l2: 0.00721585\n",
      "[81]\tvalid_0's l1: 0.0594197\tvalid_0's l2: 0.00720856\n",
      "[82]\tvalid_0's l1: 0.0593932\tvalid_0's l2: 0.00720475\n",
      "[83]\tvalid_0's l1: 0.0593157\tvalid_0's l2: 0.00718559\n",
      "[84]\tvalid_0's l1: 0.0592926\tvalid_0's l2: 0.00718174\n",
      "[85]\tvalid_0's l1: 0.0592627\tvalid_0's l2: 0.00717558\n",
      "[86]\tvalid_0's l1: 0.0592466\tvalid_0's l2: 0.00716984\n",
      "[87]\tvalid_0's l1: 0.0592043\tvalid_0's l2: 0.00716193\n",
      "[88]\tvalid_0's l1: 0.0591298\tvalid_0's l2: 0.00714342\n",
      "[89]\tvalid_0's l1: 0.059097\tvalid_0's l2: 0.00713607\n",
      "[90]\tvalid_0's l1: 0.0589982\tvalid_0's l2: 0.00711745\n",
      "[91]\tvalid_0's l1: 0.0589505\tvalid_0's l2: 0.00710413\n",
      "[92]\tvalid_0's l1: 0.0588884\tvalid_0's l2: 0.00708708\n",
      "[93]\tvalid_0's l1: 0.0588084\tvalid_0's l2: 0.00707062\n",
      "[94]\tvalid_0's l1: 0.0587967\tvalid_0's l2: 0.00707269\n",
      "[95]\tvalid_0's l1: 0.0587689\tvalid_0's l2: 0.00706628\n",
      "[96]\tvalid_0's l1: 0.058749\tvalid_0's l2: 0.00705746\n",
      "[97]\tvalid_0's l1: 0.0587121\tvalid_0's l2: 0.00704977\n",
      "[98]\tvalid_0's l1: 0.0586712\tvalid_0's l2: 0.00703874\n",
      "[99]\tvalid_0's l1: 0.05861\tvalid_0's l2: 0.0070237\n",
      "[100]\tvalid_0's l1: 0.0585889\tvalid_0's l2: 0.00701449\n",
      "[101]\tvalid_0's l1: 0.0585771\tvalid_0's l2: 0.00701003\n",
      "[102]\tvalid_0's l1: 0.0585635\tvalid_0's l2: 0.0070058\n",
      "[103]\tvalid_0's l1: 0.0585479\tvalid_0's l2: 0.00700123\n",
      "[104]\tvalid_0's l1: 0.0585384\tvalid_0's l2: 0.00699934\n",
      "[105]\tvalid_0's l1: 0.0585209\tvalid_0's l2: 0.00699689\n",
      "[106]\tvalid_0's l1: 0.0585063\tvalid_0's l2: 0.00699365\n",
      "[107]\tvalid_0's l1: 0.0584905\tvalid_0's l2: 0.00698833\n",
      "[108]\tvalid_0's l1: 0.0584738\tvalid_0's l2: 0.00698361\n",
      "[109]\tvalid_0's l1: 0.0584563\tvalid_0's l2: 0.00697852\n",
      "[110]\tvalid_0's l1: 0.0584379\tvalid_0's l2: 0.00697721\n",
      "[111]\tvalid_0's l1: 0.0584333\tvalid_0's l2: 0.00697385\n",
      "[112]\tvalid_0's l1: 0.0584316\tvalid_0's l2: 0.00697371\n",
      "[113]\tvalid_0's l1: 0.0584076\tvalid_0's l2: 0.00696515\n",
      "[114]\tvalid_0's l1: 0.0583903\tvalid_0's l2: 0.00696145\n",
      "[115]\tvalid_0's l1: 0.0583817\tvalid_0's l2: 0.00695705\n",
      "[116]\tvalid_0's l1: 0.0583585\tvalid_0's l2: 0.00695171\n",
      "[117]\tvalid_0's l1: 0.0583329\tvalid_0's l2: 0.00694439\n",
      "[118]\tvalid_0's l1: 0.0582953\tvalid_0's l2: 0.00693451\n",
      "[119]\tvalid_0's l1: 0.0582302\tvalid_0's l2: 0.00691471\n",
      "[120]\tvalid_0's l1: 0.0582053\tvalid_0's l2: 0.00690717\n",
      "[121]\tvalid_0's l1: 0.058165\tvalid_0's l2: 0.00690191\n",
      "[122]\tvalid_0's l1: 0.0581507\tvalid_0's l2: 0.00689919\n",
      "[123]\tvalid_0's l1: 0.0581202\tvalid_0's l2: 0.00689344\n",
      "[124]\tvalid_0's l1: 0.0580842\tvalid_0's l2: 0.00688742\n",
      "[125]\tvalid_0's l1: 0.0580695\tvalid_0's l2: 0.00688381\n",
      "[126]\tvalid_0's l1: 0.0580556\tvalid_0's l2: 0.0068812\n",
      "[127]\tvalid_0's l1: 0.0580404\tvalid_0's l2: 0.00687439\n",
      "[128]\tvalid_0's l1: 0.0580276\tvalid_0's l2: 0.00687128\n",
      "[129]\tvalid_0's l1: 0.0579982\tvalid_0's l2: 0.00686654\n",
      "[130]\tvalid_0's l1: 0.0579886\tvalid_0's l2: 0.00686163\n",
      "[131]\tvalid_0's l1: 0.0579795\tvalid_0's l2: 0.00686265\n",
      "[132]\tvalid_0's l1: 0.0579773\tvalid_0's l2: 0.00686113\n",
      "[133]\tvalid_0's l1: 0.0579718\tvalid_0's l2: 0.00686023\n",
      "[134]\tvalid_0's l1: 0.0579558\tvalid_0's l2: 0.00685794\n",
      "[135]\tvalid_0's l1: 0.0579452\tvalid_0's l2: 0.00685478\n",
      "[136]\tvalid_0's l1: 0.0579375\tvalid_0's l2: 0.00685219\n",
      "[137]\tvalid_0's l1: 0.0579311\tvalid_0's l2: 0.00685004\n",
      "[138]\tvalid_0's l1: 0.0578929\tvalid_0's l2: 0.0068395\n",
      "[139]\tvalid_0's l1: 0.0578863\tvalid_0's l2: 0.00683737\n",
      "[140]\tvalid_0's l1: 0.0578618\tvalid_0's l2: 0.00682973\n",
      "[141]\tvalid_0's l1: 0.057814\tvalid_0's l2: 0.00682006\n",
      "[142]\tvalid_0's l1: 0.0577865\tvalid_0's l2: 0.00681178\n",
      "[143]\tvalid_0's l1: 0.0577836\tvalid_0's l2: 0.00681092\n",
      "[144]\tvalid_0's l1: 0.0577732\tvalid_0's l2: 0.00680807\n",
      "[145]\tvalid_0's l1: 0.05775\tvalid_0's l2: 0.00680476\n",
      "[146]\tvalid_0's l1: 0.0577295\tvalid_0's l2: 0.00679983\n",
      "[147]\tvalid_0's l1: 0.0577274\tvalid_0's l2: 0.00679906\n",
      "[148]\tvalid_0's l1: 0.0577282\tvalid_0's l2: 0.00680071\n",
      "[149]\tvalid_0's l1: 0.0577027\tvalid_0's l2: 0.00679154\n",
      "[150]\tvalid_0's l1: 0.0577023\tvalid_0's l2: 0.00679031\n",
      "[151]\tvalid_0's l1: 0.0576895\tvalid_0's l2: 0.0067869\n",
      "[152]\tvalid_0's l1: 0.0576863\tvalid_0's l2: 0.00678669\n",
      "[153]\tvalid_0's l1: 0.057681\tvalid_0's l2: 0.00678298\n",
      "[154]\tvalid_0's l1: 0.0576461\tvalid_0's l2: 0.00677587\n",
      "[155]\tvalid_0's l1: 0.0576281\tvalid_0's l2: 0.00676922\n",
      "[156]\tvalid_0's l1: 0.0576071\tvalid_0's l2: 0.00676582\n",
      "[157]\tvalid_0's l1: 0.0575831\tvalid_0's l2: 0.00676433\n",
      "[158]\tvalid_0's l1: 0.0575486\tvalid_0's l2: 0.00675594\n",
      "[159]\tvalid_0's l1: 0.0575371\tvalid_0's l2: 0.00675428\n",
      "[160]\tvalid_0's l1: 0.0575191\tvalid_0's l2: 0.00675119\n",
      "[161]\tvalid_0's l1: 0.0575124\tvalid_0's l2: 0.00675132\n",
      "[162]\tvalid_0's l1: 0.0575015\tvalid_0's l2: 0.00674851\n",
      "[163]\tvalid_0's l1: 0.0574785\tvalid_0's l2: 0.00674612\n",
      "[164]\tvalid_0's l1: 0.0574678\tvalid_0's l2: 0.00674357\n",
      "[165]\tvalid_0's l1: 0.0574633\tvalid_0's l2: 0.00674366\n",
      "[166]\tvalid_0's l1: 0.0574458\tvalid_0's l2: 0.00673905\n",
      "[167]\tvalid_0's l1: 0.0574112\tvalid_0's l2: 0.00672946\n",
      "[168]\tvalid_0's l1: 0.0573985\tvalid_0's l2: 0.00672684\n",
      "[169]\tvalid_0's l1: 0.0573967\tvalid_0's l2: 0.00672339\n",
      "[170]\tvalid_0's l1: 0.0573916\tvalid_0's l2: 0.00672143\n",
      "[171]\tvalid_0's l1: 0.0573821\tvalid_0's l2: 0.00671896\n",
      "[172]\tvalid_0's l1: 0.0573703\tvalid_0's l2: 0.00671675\n",
      "[173]\tvalid_0's l1: 0.0573778\tvalid_0's l2: 0.00671835\n",
      "[174]\tvalid_0's l1: 0.0573707\tvalid_0's l2: 0.00671666\n",
      "[175]\tvalid_0's l1: 0.0573572\tvalid_0's l2: 0.00671479\n",
      "[176]\tvalid_0's l1: 0.0573574\tvalid_0's l2: 0.0067135\n",
      "[177]\tvalid_0's l1: 0.0573542\tvalid_0's l2: 0.00671297\n",
      "[178]\tvalid_0's l1: 0.057355\tvalid_0's l2: 0.00671458\n",
      "[179]\tvalid_0's l1: 0.0573403\tvalid_0's l2: 0.00671148\n",
      "[180]\tvalid_0's l1: 0.0573278\tvalid_0's l2: 0.00670699\n",
      "[181]\tvalid_0's l1: 0.0573277\tvalid_0's l2: 0.00670656\n",
      "[182]\tvalid_0's l1: 0.0573192\tvalid_0's l2: 0.00670432\n",
      "[183]\tvalid_0's l1: 0.0573012\tvalid_0's l2: 0.00670044\n",
      "[184]\tvalid_0's l1: 0.0573018\tvalid_0's l2: 0.00669917\n",
      "[185]\tvalid_0's l1: 0.0572933\tvalid_0's l2: 0.00669791\n",
      "[186]\tvalid_0's l1: 0.0572937\tvalid_0's l2: 0.00669628\n",
      "[187]\tvalid_0's l1: 0.0572959\tvalid_0's l2: 0.006696\n",
      "[188]\tvalid_0's l1: 0.0572946\tvalid_0's l2: 0.00669529\n",
      "[189]\tvalid_0's l1: 0.057284\tvalid_0's l2: 0.00669419\n",
      "[190]\tvalid_0's l1: 0.0572756\tvalid_0's l2: 0.00669183\n",
      "[191]\tvalid_0's l1: 0.0572536\tvalid_0's l2: 0.00668716\n",
      "[192]\tvalid_0's l1: 0.0572549\tvalid_0's l2: 0.0066889\n",
      "[193]\tvalid_0's l1: 0.0572407\tvalid_0's l2: 0.00668565\n",
      "[194]\tvalid_0's l1: 0.0572137\tvalid_0's l2: 0.00668092\n",
      "[195]\tvalid_0's l1: 0.0572176\tvalid_0's l2: 0.00668088\n",
      "[196]\tvalid_0's l1: 0.0571987\tvalid_0's l2: 0.00667881\n",
      "[197]\tvalid_0's l1: 0.0571904\tvalid_0's l2: 0.00667394\n",
      "[198]\tvalid_0's l1: 0.0571825\tvalid_0's l2: 0.00667086\n",
      "[199]\tvalid_0's l1: 0.0571814\tvalid_0's l2: 0.0066715\n",
      "[200]\tvalid_0's l1: 0.0571754\tvalid_0's l2: 0.0066712\n",
      "[201]\tvalid_0's l1: 0.0571679\tvalid_0's l2: 0.00666813\n",
      "[202]\tvalid_0's l1: 0.0571538\tvalid_0's l2: 0.00666858\n",
      "[203]\tvalid_0's l1: 0.0571505\tvalid_0's l2: 0.00666737\n",
      "[204]\tvalid_0's l1: 0.0571496\tvalid_0's l2: 0.00666642\n",
      "[205]\tvalid_0's l1: 0.0571438\tvalid_0's l2: 0.00666369\n",
      "[206]\tvalid_0's l1: 0.0571522\tvalid_0's l2: 0.0066647\n",
      "[207]\tvalid_0's l1: 0.0571443\tvalid_0's l2: 0.00666353\n",
      "[208]\tvalid_0's l1: 0.057147\tvalid_0's l2: 0.00666341\n",
      "[209]\tvalid_0's l1: 0.0571413\tvalid_0's l2: 0.00666194\n",
      "[210]\tvalid_0's l1: 0.0571264\tvalid_0's l2: 0.00666014\n",
      "[211]\tvalid_0's l1: 0.0571122\tvalid_0's l2: 0.00665585\n",
      "[212]\tvalid_0's l1: 0.0571118\tvalid_0's l2: 0.00665518\n",
      "[213]\tvalid_0's l1: 0.0571002\tvalid_0's l2: 0.00665338\n",
      "[214]\tvalid_0's l1: 0.0570964\tvalid_0's l2: 0.00665241\n",
      "[215]\tvalid_0's l1: 0.05709\tvalid_0's l2: 0.00665055\n",
      "[216]\tvalid_0's l1: 0.0570856\tvalid_0's l2: 0.00665057\n",
      "[217]\tvalid_0's l1: 0.0570826\tvalid_0's l2: 0.00665004\n",
      "[218]\tvalid_0's l1: 0.0570747\tvalid_0's l2: 0.00664898\n",
      "[219]\tvalid_0's l1: 0.057068\tvalid_0's l2: 0.00664791\n",
      "[220]\tvalid_0's l1: 0.05707\tvalid_0's l2: 0.00664691\n",
      "[221]\tvalid_0's l1: 0.0570675\tvalid_0's l2: 0.00664523\n",
      "[222]\tvalid_0's l1: 0.0570426\tvalid_0's l2: 0.00664251\n",
      "[223]\tvalid_0's l1: 0.0570167\tvalid_0's l2: 0.00663508\n",
      "[224]\tvalid_0's l1: 0.0570101\tvalid_0's l2: 0.00663261\n",
      "[225]\tvalid_0's l1: 0.0570103\tvalid_0's l2: 0.00663457\n",
      "[226]\tvalid_0's l1: 0.0569972\tvalid_0's l2: 0.00663136\n",
      "[227]\tvalid_0's l1: 0.0569882\tvalid_0's l2: 0.00663009\n",
      "[228]\tvalid_0's l1: 0.0569898\tvalid_0's l2: 0.00662954\n",
      "[229]\tvalid_0's l1: 0.0569865\tvalid_0's l2: 0.00662705\n",
      "[230]\tvalid_0's l1: 0.0569757\tvalid_0's l2: 0.00662507\n",
      "[231]\tvalid_0's l1: 0.0569731\tvalid_0's l2: 0.00662383\n",
      "[232]\tvalid_0's l1: 0.0569676\tvalid_0's l2: 0.00662384\n",
      "[233]\tvalid_0's l1: 0.0569664\tvalid_0's l2: 0.00662459\n",
      "[234]\tvalid_0's l1: 0.0569611\tvalid_0's l2: 0.00662457\n",
      "[235]\tvalid_0's l1: 0.0569633\tvalid_0's l2: 0.00662413\n",
      "[236]\tvalid_0's l1: 0.0569615\tvalid_0's l2: 0.00662337\n",
      "[237]\tvalid_0's l1: 0.0569616\tvalid_0's l2: 0.00662352\n",
      "[238]\tvalid_0's l1: 0.0569541\tvalid_0's l2: 0.00662248\n",
      "[239]\tvalid_0's l1: 0.0569478\tvalid_0's l2: 0.0066219\n",
      "[240]\tvalid_0's l1: 0.0569487\tvalid_0's l2: 0.00662202\n",
      "[241]\tvalid_0's l1: 0.0569517\tvalid_0's l2: 0.00662222\n",
      "[242]\tvalid_0's l1: 0.0569443\tvalid_0's l2: 0.00662104\n",
      "[243]\tvalid_0's l1: 0.0569249\tvalid_0's l2: 0.00661558\n",
      "[244]\tvalid_0's l1: 0.0569245\tvalid_0's l2: 0.00661384\n",
      "[245]\tvalid_0's l1: 0.0569247\tvalid_0's l2: 0.00661414\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[246]\tvalid_0's l1: 0.0569229\tvalid_0's l2: 0.00661358\n",
      "[247]\tvalid_0's l1: 0.0569195\tvalid_0's l2: 0.00661233\n",
      "[248]\tvalid_0's l1: 0.0569077\tvalid_0's l2: 0.00660972\n",
      "[249]\tvalid_0's l1: 0.0569058\tvalid_0's l2: 0.00660907\n",
      "[250]\tvalid_0's l1: 0.0569022\tvalid_0's l2: 0.00660765\n",
      "[251]\tvalid_0's l1: 0.056903\tvalid_0's l2: 0.00660776\n",
      "[252]\tvalid_0's l1: 0.0569011\tvalid_0's l2: 0.00660744\n",
      "[253]\tvalid_0's l1: 0.0569056\tvalid_0's l2: 0.00660932\n",
      "[254]\tvalid_0's l1: 0.0569014\tvalid_0's l2: 0.00660959\n",
      "[255]\tvalid_0's l1: 0.0568977\tvalid_0's l2: 0.00660912\n",
      "[256]\tvalid_0's l1: 0.0568991\tvalid_0's l2: 0.00660947\n",
      "[257]\tvalid_0's l1: 0.0568659\tvalid_0's l2: 0.00660135\n",
      "[258]\tvalid_0's l1: 0.0568594\tvalid_0's l2: 0.00660015\n",
      "[259]\tvalid_0's l1: 0.0568588\tvalid_0's l2: 0.00659937\n",
      "[260]\tvalid_0's l1: 0.0568588\tvalid_0's l2: 0.00659942\n",
      "[261]\tvalid_0's l1: 0.056858\tvalid_0's l2: 0.00659948\n",
      "[262]\tvalid_0's l1: 0.0568521\tvalid_0's l2: 0.00659886\n",
      "[263]\tvalid_0's l1: 0.0568516\tvalid_0's l2: 0.00659866\n",
      "[264]\tvalid_0's l1: 0.0568237\tvalid_0's l2: 0.00659403\n",
      "[265]\tvalid_0's l1: 0.0568185\tvalid_0's l2: 0.00659247\n",
      "[266]\tvalid_0's l1: 0.0568207\tvalid_0's l2: 0.00659355\n",
      "[267]\tvalid_0's l1: 0.0568153\tvalid_0's l2: 0.00659291\n",
      "[268]\tvalid_0's l1: 0.0568104\tvalid_0's l2: 0.00659252\n",
      "[269]\tvalid_0's l1: 0.0568035\tvalid_0's l2: 0.00659123\n",
      "[270]\tvalid_0's l1: 0.0567872\tvalid_0's l2: 0.00658465\n",
      "[271]\tvalid_0's l1: 0.0567721\tvalid_0's l2: 0.00658252\n",
      "[272]\tvalid_0's l1: 0.0567615\tvalid_0's l2: 0.00658002\n",
      "[273]\tvalid_0's l1: 0.0567417\tvalid_0's l2: 0.00657648\n",
      "[274]\tvalid_0's l1: 0.0567421\tvalid_0's l2: 0.00657556\n",
      "[275]\tvalid_0's l1: 0.0567452\tvalid_0's l2: 0.00657642\n",
      "[276]\tvalid_0's l1: 0.0567469\tvalid_0's l2: 0.00657607\n",
      "[277]\tvalid_0's l1: 0.056743\tvalid_0's l2: 0.00657593\n",
      "[278]\tvalid_0's l1: 0.0567391\tvalid_0's l2: 0.00657495\n",
      "[279]\tvalid_0's l1: 0.056739\tvalid_0's l2: 0.00657478\n",
      "[280]\tvalid_0's l1: 0.0567325\tvalid_0's l2: 0.00657337\n",
      "[281]\tvalid_0's l1: 0.0567323\tvalid_0's l2: 0.00657334\n",
      "[282]\tvalid_0's l1: 0.0567366\tvalid_0's l2: 0.00657365\n",
      "[283]\tvalid_0's l1: 0.0567364\tvalid_0's l2: 0.00657325\n",
      "[284]\tvalid_0's l1: 0.0567233\tvalid_0's l2: 0.00657079\n",
      "[285]\tvalid_0's l1: 0.0567246\tvalid_0's l2: 0.0065718\n",
      "[286]\tvalid_0's l1: 0.0567265\tvalid_0's l2: 0.00657222\n",
      "[287]\tvalid_0's l1: 0.0567223\tvalid_0's l2: 0.00657141\n",
      "[288]\tvalid_0's l1: 0.056714\tvalid_0's l2: 0.00656963\n",
      "[289]\tvalid_0's l1: 0.0567092\tvalid_0's l2: 0.00656967\n",
      "[290]\tvalid_0's l1: 0.0566989\tvalid_0's l2: 0.00656775\n",
      "[291]\tvalid_0's l1: 0.0566969\tvalid_0's l2: 0.00656714\n",
      "[292]\tvalid_0's l1: 0.0566972\tvalid_0's l2: 0.00656797\n",
      "[293]\tvalid_0's l1: 0.0566986\tvalid_0's l2: 0.00656812\n",
      "[294]\tvalid_0's l1: 0.056693\tvalid_0's l2: 0.00656709\n",
      "[295]\tvalid_0's l1: 0.0566943\tvalid_0's l2: 0.00656743\n",
      "[296]\tvalid_0's l1: 0.0566987\tvalid_0's l2: 0.0065679\n",
      "[297]\tvalid_0's l1: 0.056698\tvalid_0's l2: 0.00656738\n",
      "[298]\tvalid_0's l1: 0.0566927\tvalid_0's l2: 0.00656645\n",
      "[299]\tvalid_0's l1: 0.0566939\tvalid_0's l2: 0.00656589\n",
      "[300]\tvalid_0's l1: 0.0566834\tvalid_0's l2: 0.00656346\n",
      "Did not meet early stopping. Best iteration is:\n",
      "[300]\tvalid_0's l1: 0.0566834\tvalid_0's l2: 0.00656346\n",
      "本次结果输出的mae值是:\n",
      " 0.056683443527655\n",
      "[1]\tvalid_0's l1: 0.244506\tvalid_0's l2: 0.0790859\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[2]\tvalid_0's l1: 0.223857\tvalid_0's l2: 0.0667341\n",
      "[3]\tvalid_0's l1: 0.205626\tvalid_0's l2: 0.0566941\n",
      "[4]\tvalid_0's l1: 0.189298\tvalid_0's l2: 0.0484539\n",
      "[5]\tvalid_0's l1: 0.175104\tvalid_0's l2: 0.0417921\n",
      "[6]\tvalid_0's l1: 0.16227\tvalid_0's l2: 0.036285\n",
      "[7]\tvalid_0's l1: 0.150898\tvalid_0's l2: 0.0317221\n",
      "[8]\tvalid_0's l1: 0.140883\tvalid_0's l2: 0.0280196\n",
      "[9]\tvalid_0's l1: 0.131939\tvalid_0's l2: 0.0249613\n",
      "[10]\tvalid_0's l1: 0.124375\tvalid_0's l2: 0.0224944\n",
      "[11]\tvalid_0's l1: 0.117467\tvalid_0's l2: 0.0204263\n",
      "[12]\tvalid_0's l1: 0.110957\tvalid_0's l2: 0.0185453\n",
      "[13]\tvalid_0's l1: 0.105077\tvalid_0's l2: 0.016929\n",
      "[14]\tvalid_0's l1: 0.100272\tvalid_0's l2: 0.0157152\n",
      "[15]\tvalid_0's l1: 0.0960833\tvalid_0's l2: 0.0147431\n",
      "[16]\tvalid_0's l1: 0.0920567\tvalid_0's l2: 0.0137979\n",
      "[17]\tvalid_0's l1: 0.088268\tvalid_0's l2: 0.0129121\n",
      "[18]\tvalid_0's l1: 0.0854499\tvalid_0's l2: 0.0123437\n",
      "[19]\tvalid_0's l1: 0.0827065\tvalid_0's l2: 0.0117755\n",
      "[20]\tvalid_0's l1: 0.0800967\tvalid_0's l2: 0.0112162\n",
      "[21]\tvalid_0's l1: 0.0781237\tvalid_0's l2: 0.0108399\n",
      "[22]\tvalid_0's l1: 0.0763776\tvalid_0's l2: 0.0105204\n",
      "[23]\tvalid_0's l1: 0.074906\tvalid_0's l2: 0.010265\n",
      "[24]\tvalid_0's l1: 0.0732985\tvalid_0's l2: 0.00994854\n",
      "[25]\tvalid_0's l1: 0.0720481\tvalid_0's l2: 0.00970346\n",
      "[26]\tvalid_0's l1: 0.0710837\tvalid_0's l2: 0.00953733\n",
      "[27]\tvalid_0's l1: 0.0703066\tvalid_0's l2: 0.00939487\n",
      "[28]\tvalid_0's l1: 0.0694075\tvalid_0's l2: 0.009231\n",
      "[29]\tvalid_0's l1: 0.0685866\tvalid_0's l2: 0.00907982\n",
      "[30]\tvalid_0's l1: 0.06779\tvalid_0's l2: 0.0089391\n",
      "[31]\tvalid_0's l1: 0.067074\tvalid_0's l2: 0.00880224\n",
      "[32]\tvalid_0's l1: 0.0665149\tvalid_0's l2: 0.00871073\n",
      "[33]\tvalid_0's l1: 0.0659205\tvalid_0's l2: 0.00858992\n",
      "[34]\tvalid_0's l1: 0.0652426\tvalid_0's l2: 0.00843778\n",
      "[35]\tvalid_0's l1: 0.0648768\tvalid_0's l2: 0.00836372\n",
      "[36]\tvalid_0's l1: 0.0645306\tvalid_0's l2: 0.00830614\n",
      "[37]\tvalid_0's l1: 0.0640175\tvalid_0's l2: 0.00818645\n",
      "[38]\tvalid_0's l1: 0.0637947\tvalid_0's l2: 0.00814636\n",
      "[39]\tvalid_0's l1: 0.0635605\tvalid_0's l2: 0.00810447\n",
      "[40]\tvalid_0's l1: 0.0633449\tvalid_0's l2: 0.00806796\n",
      "[41]\tvalid_0's l1: 0.0631446\tvalid_0's l2: 0.00802436\n",
      "[42]\tvalid_0's l1: 0.0627575\tvalid_0's l2: 0.00792715\n",
      "[43]\tvalid_0's l1: 0.062611\tvalid_0's l2: 0.00789809\n",
      "[44]\tvalid_0's l1: 0.0623997\tvalid_0's l2: 0.00785422\n",
      "[45]\tvalid_0's l1: 0.0622728\tvalid_0's l2: 0.00783369\n",
      "[46]\tvalid_0's l1: 0.0621336\tvalid_0's l2: 0.00781042\n",
      "[47]\tvalid_0's l1: 0.0619906\tvalid_0's l2: 0.00777463\n",
      "[48]\tvalid_0's l1: 0.0618556\tvalid_0's l2: 0.00774471\n",
      "[49]\tvalid_0's l1: 0.0617449\tvalid_0's l2: 0.00772713\n",
      "[50]\tvalid_0's l1: 0.0616454\tvalid_0's l2: 0.00770345\n",
      "[51]\tvalid_0's l1: 0.0615492\tvalid_0's l2: 0.00768433\n",
      "[52]\tvalid_0's l1: 0.0614452\tvalid_0's l2: 0.00766572\n",
      "[53]\tvalid_0's l1: 0.0613725\tvalid_0's l2: 0.00764408\n",
      "[54]\tvalid_0's l1: 0.0612796\tvalid_0's l2: 0.00762834\n",
      "[55]\tvalid_0's l1: 0.0611706\tvalid_0's l2: 0.00760066\n",
      "[56]\tvalid_0's l1: 0.0611121\tvalid_0's l2: 0.00759012\n",
      "[57]\tvalid_0's l1: 0.0610472\tvalid_0's l2: 0.00757864\n",
      "[58]\tvalid_0's l1: 0.0609801\tvalid_0's l2: 0.00756664\n",
      "[59]\tvalid_0's l1: 0.0608993\tvalid_0's l2: 0.00755207\n",
      "[60]\tvalid_0's l1: 0.0608206\tvalid_0's l2: 0.00753343\n",
      "[61]\tvalid_0's l1: 0.0607751\tvalid_0's l2: 0.00752435\n",
      "[62]\tvalid_0's l1: 0.0607163\tvalid_0's l2: 0.00751128\n",
      "[63]\tvalid_0's l1: 0.0605746\tvalid_0's l2: 0.00747752\n",
      "[64]\tvalid_0's l1: 0.0604651\tvalid_0's l2: 0.0074507\n",
      "[65]\tvalid_0's l1: 0.0604263\tvalid_0's l2: 0.00744162\n",
      "[66]\tvalid_0's l1: 0.0603599\tvalid_0's l2: 0.00742889\n",
      "[67]\tvalid_0's l1: 0.0602701\tvalid_0's l2: 0.00741169\n",
      "[68]\tvalid_0's l1: 0.0602334\tvalid_0's l2: 0.00739977\n",
      "[69]\tvalid_0's l1: 0.060112\tvalid_0's l2: 0.007374\n",
      "[70]\tvalid_0's l1: 0.0600257\tvalid_0's l2: 0.00735234\n",
      "[71]\tvalid_0's l1: 0.059928\tvalid_0's l2: 0.00732775\n",
      "[72]\tvalid_0's l1: 0.0598568\tvalid_0's l2: 0.00731406\n",
      "[73]\tvalid_0's l1: 0.0598019\tvalid_0's l2: 0.007307\n",
      "[74]\tvalid_0's l1: 0.0597175\tvalid_0's l2: 0.00728248\n",
      "[75]\tvalid_0's l1: 0.0596246\tvalid_0's l2: 0.00726385\n",
      "[76]\tvalid_0's l1: 0.0596029\tvalid_0's l2: 0.00725546\n",
      "[77]\tvalid_0's l1: 0.0595231\tvalid_0's l2: 0.00723235\n",
      "[78]\tvalid_0's l1: 0.0595014\tvalid_0's l2: 0.00722831\n",
      "[79]\tvalid_0's l1: 0.0594712\tvalid_0's l2: 0.00722155\n",
      "[80]\tvalid_0's l1: 0.0594512\tvalid_0's l2: 0.00721585\n",
      "[81]\tvalid_0's l1: 0.0594197\tvalid_0's l2: 0.00720856\n",
      "[82]\tvalid_0's l1: 0.0593932\tvalid_0's l2: 0.00720475\n",
      "[83]\tvalid_0's l1: 0.0593157\tvalid_0's l2: 0.00718559\n",
      "[84]\tvalid_0's l1: 0.0592926\tvalid_0's l2: 0.00718174\n",
      "[85]\tvalid_0's l1: 0.0592627\tvalid_0's l2: 0.00717558\n",
      "[86]\tvalid_0's l1: 0.0592466\tvalid_0's l2: 0.00716984\n",
      "[87]\tvalid_0's l1: 0.0592043\tvalid_0's l2: 0.00716193\n",
      "[88]\tvalid_0's l1: 0.0591298\tvalid_0's l2: 0.00714342\n",
      "[89]\tvalid_0's l1: 0.059097\tvalid_0's l2: 0.00713607\n",
      "[90]\tvalid_0's l1: 0.0589982\tvalid_0's l2: 0.00711745\n",
      "[91]\tvalid_0's l1: 0.0589505\tvalid_0's l2: 0.00710413\n",
      "[92]\tvalid_0's l1: 0.0588884\tvalid_0's l2: 0.00708708\n",
      "[93]\tvalid_0's l1: 0.0588084\tvalid_0's l2: 0.00707062\n",
      "[94]\tvalid_0's l1: 0.0587967\tvalid_0's l2: 0.00707269\n",
      "[95]\tvalid_0's l1: 0.0587689\tvalid_0's l2: 0.00706628\n",
      "[96]\tvalid_0's l1: 0.058749\tvalid_0's l2: 0.00705746\n",
      "[97]\tvalid_0's l1: 0.0587121\tvalid_0's l2: 0.00704977\n",
      "[98]\tvalid_0's l1: 0.0586712\tvalid_0's l2: 0.00703874\n",
      "[99]\tvalid_0's l1: 0.05861\tvalid_0's l2: 0.0070237\n",
      "[100]\tvalid_0's l1: 0.0585889\tvalid_0's l2: 0.00701449\n",
      "[101]\tvalid_0's l1: 0.0585771\tvalid_0's l2: 0.00701003\n",
      "[102]\tvalid_0's l1: 0.0585635\tvalid_0's l2: 0.0070058\n",
      "[103]\tvalid_0's l1: 0.0585479\tvalid_0's l2: 0.00700123\n",
      "[104]\tvalid_0's l1: 0.0585384\tvalid_0's l2: 0.00699934\n",
      "[105]\tvalid_0's l1: 0.0585209\tvalid_0's l2: 0.00699689\n",
      "[106]\tvalid_0's l1: 0.0585063\tvalid_0's l2: 0.00699365\n",
      "[107]\tvalid_0's l1: 0.0584905\tvalid_0's l2: 0.00698833\n",
      "[108]\tvalid_0's l1: 0.0584738\tvalid_0's l2: 0.00698361\n",
      "[109]\tvalid_0's l1: 0.0584563\tvalid_0's l2: 0.00697852\n",
      "[110]\tvalid_0's l1: 0.0584379\tvalid_0's l2: 0.00697721\n",
      "[111]\tvalid_0's l1: 0.0584333\tvalid_0's l2: 0.00697385\n",
      "[112]\tvalid_0's l1: 0.0584316\tvalid_0's l2: 0.00697371\n",
      "[113]\tvalid_0's l1: 0.0584076\tvalid_0's l2: 0.00696515\n",
      "[114]\tvalid_0's l1: 0.0583903\tvalid_0's l2: 0.00696145\n",
      "[115]\tvalid_0's l1: 0.0583817\tvalid_0's l2: 0.00695705\n",
      "[116]\tvalid_0's l1: 0.0583585\tvalid_0's l2: 0.00695171\n",
      "[117]\tvalid_0's l1: 0.0583329\tvalid_0's l2: 0.00694439\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[118]\tvalid_0's l1: 0.0582953\tvalid_0's l2: 0.00693451\n",
      "[119]\tvalid_0's l1: 0.0582302\tvalid_0's l2: 0.00691471\n",
      "[120]\tvalid_0's l1: 0.0582053\tvalid_0's l2: 0.00690717\n",
      "[121]\tvalid_0's l1: 0.058165\tvalid_0's l2: 0.00690191\n",
      "[122]\tvalid_0's l1: 0.0581507\tvalid_0's l2: 0.00689919\n",
      "[123]\tvalid_0's l1: 0.0581202\tvalid_0's l2: 0.00689344\n",
      "[124]\tvalid_0's l1: 0.0580842\tvalid_0's l2: 0.00688742\n",
      "[125]\tvalid_0's l1: 0.0580695\tvalid_0's l2: 0.00688381\n",
      "[126]\tvalid_0's l1: 0.0580556\tvalid_0's l2: 0.0068812\n",
      "[127]\tvalid_0's l1: 0.0580404\tvalid_0's l2: 0.00687439\n",
      "[128]\tvalid_0's l1: 0.0580276\tvalid_0's l2: 0.00687128\n",
      "[129]\tvalid_0's l1: 0.0579982\tvalid_0's l2: 0.00686654\n",
      "[130]\tvalid_0's l1: 0.0579886\tvalid_0's l2: 0.00686163\n",
      "[131]\tvalid_0's l1: 0.0579795\tvalid_0's l2: 0.00686265\n",
      "[132]\tvalid_0's l1: 0.0579773\tvalid_0's l2: 0.00686113\n",
      "[133]\tvalid_0's l1: 0.0579718\tvalid_0's l2: 0.00686023\n",
      "[134]\tvalid_0's l1: 0.0579558\tvalid_0's l2: 0.00685794\n",
      "[135]\tvalid_0's l1: 0.0579452\tvalid_0's l2: 0.00685478\n",
      "[136]\tvalid_0's l1: 0.0579375\tvalid_0's l2: 0.00685219\n",
      "[137]\tvalid_0's l1: 0.0579311\tvalid_0's l2: 0.00685004\n",
      "[138]\tvalid_0's l1: 0.0578929\tvalid_0's l2: 0.0068395\n",
      "[139]\tvalid_0's l1: 0.0578863\tvalid_0's l2: 0.00683737\n",
      "[140]\tvalid_0's l1: 0.0578618\tvalid_0's l2: 0.00682973\n",
      "[141]\tvalid_0's l1: 0.057814\tvalid_0's l2: 0.00682006\n",
      "[142]\tvalid_0's l1: 0.0577865\tvalid_0's l2: 0.00681178\n",
      "[143]\tvalid_0's l1: 0.0577836\tvalid_0's l2: 0.00681092\n",
      "[144]\tvalid_0's l1: 0.0577732\tvalid_0's l2: 0.00680807\n",
      "[145]\tvalid_0's l1: 0.05775\tvalid_0's l2: 0.00680476\n",
      "[146]\tvalid_0's l1: 0.0577295\tvalid_0's l2: 0.00679983\n",
      "[147]\tvalid_0's l1: 0.0577274\tvalid_0's l2: 0.00679906\n",
      "[148]\tvalid_0's l1: 0.0577282\tvalid_0's l2: 0.00680071\n",
      "[149]\tvalid_0's l1: 0.0577027\tvalid_0's l2: 0.00679154\n",
      "[150]\tvalid_0's l1: 0.0577023\tvalid_0's l2: 0.00679031\n",
      "[151]\tvalid_0's l1: 0.0576895\tvalid_0's l2: 0.0067869\n",
      "[152]\tvalid_0's l1: 0.0576863\tvalid_0's l2: 0.00678669\n",
      "[153]\tvalid_0's l1: 0.057681\tvalid_0's l2: 0.00678298\n",
      "[154]\tvalid_0's l1: 0.0576461\tvalid_0's l2: 0.00677587\n",
      "[155]\tvalid_0's l1: 0.0576281\tvalid_0's l2: 0.00676922\n",
      "[156]\tvalid_0's l1: 0.0576071\tvalid_0's l2: 0.00676582\n",
      "[157]\tvalid_0's l1: 0.0575831\tvalid_0's l2: 0.00676433\n",
      "[158]\tvalid_0's l1: 0.0575486\tvalid_0's l2: 0.00675594\n",
      "[159]\tvalid_0's l1: 0.0575371\tvalid_0's l2: 0.00675428\n",
      "[160]\tvalid_0's l1: 0.0575191\tvalid_0's l2: 0.00675119\n",
      "[161]\tvalid_0's l1: 0.0575124\tvalid_0's l2: 0.00675132\n",
      "[162]\tvalid_0's l1: 0.0575015\tvalid_0's l2: 0.00674851\n",
      "[163]\tvalid_0's l1: 0.0574785\tvalid_0's l2: 0.00674612\n",
      "[164]\tvalid_0's l1: 0.0574678\tvalid_0's l2: 0.00674357\n",
      "[165]\tvalid_0's l1: 0.0574633\tvalid_0's l2: 0.00674366\n",
      "[166]\tvalid_0's l1: 0.0574458\tvalid_0's l2: 0.00673905\n",
      "[167]\tvalid_0's l1: 0.0574112\tvalid_0's l2: 0.00672946\n",
      "[168]\tvalid_0's l1: 0.0573985\tvalid_0's l2: 0.00672684\n",
      "[169]\tvalid_0's l1: 0.0573967\tvalid_0's l2: 0.00672339\n",
      "[170]\tvalid_0's l1: 0.0573916\tvalid_0's l2: 0.00672143\n",
      "[171]\tvalid_0's l1: 0.0573821\tvalid_0's l2: 0.00671896\n",
      "[172]\tvalid_0's l1: 0.0573703\tvalid_0's l2: 0.00671675\n",
      "[173]\tvalid_0's l1: 0.0573778\tvalid_0's l2: 0.00671835\n",
      "[174]\tvalid_0's l1: 0.0573707\tvalid_0's l2: 0.00671666\n",
      "[175]\tvalid_0's l1: 0.0573572\tvalid_0's l2: 0.00671479\n",
      "[176]\tvalid_0's l1: 0.0573574\tvalid_0's l2: 0.0067135\n",
      "[177]\tvalid_0's l1: 0.0573542\tvalid_0's l2: 0.00671297\n",
      "[178]\tvalid_0's l1: 0.057355\tvalid_0's l2: 0.00671458\n",
      "[179]\tvalid_0's l1: 0.0573403\tvalid_0's l2: 0.00671148\n",
      "[180]\tvalid_0's l1: 0.0573278\tvalid_0's l2: 0.00670699\n",
      "[181]\tvalid_0's l1: 0.0573277\tvalid_0's l2: 0.00670656\n",
      "[182]\tvalid_0's l1: 0.0573192\tvalid_0's l2: 0.00670432\n",
      "[183]\tvalid_0's l1: 0.0573012\tvalid_0's l2: 0.00670044\n",
      "[184]\tvalid_0's l1: 0.0573018\tvalid_0's l2: 0.00669917\n",
      "[185]\tvalid_0's l1: 0.0572933\tvalid_0's l2: 0.00669791\n",
      "[186]\tvalid_0's l1: 0.0572937\tvalid_0's l2: 0.00669628\n",
      "[187]\tvalid_0's l1: 0.0572959\tvalid_0's l2: 0.006696\n",
      "[188]\tvalid_0's l1: 0.0572946\tvalid_0's l2: 0.00669529\n",
      "[189]\tvalid_0's l1: 0.057284\tvalid_0's l2: 0.00669419\n",
      "[190]\tvalid_0's l1: 0.0572756\tvalid_0's l2: 0.00669183\n",
      "[191]\tvalid_0's l1: 0.0572536\tvalid_0's l2: 0.00668716\n",
      "[192]\tvalid_0's l1: 0.0572549\tvalid_0's l2: 0.0066889\n",
      "[193]\tvalid_0's l1: 0.0572407\tvalid_0's l2: 0.00668565\n",
      "[194]\tvalid_0's l1: 0.0572137\tvalid_0's l2: 0.00668092\n",
      "[195]\tvalid_0's l1: 0.0572176\tvalid_0's l2: 0.00668088\n",
      "[196]\tvalid_0's l1: 0.0571987\tvalid_0's l2: 0.00667881\n",
      "[197]\tvalid_0's l1: 0.0571904\tvalid_0's l2: 0.00667394\n",
      "[198]\tvalid_0's l1: 0.0571825\tvalid_0's l2: 0.00667086\n",
      "[199]\tvalid_0's l1: 0.0571814\tvalid_0's l2: 0.0066715\n",
      "[200]\tvalid_0's l1: 0.0571754\tvalid_0's l2: 0.0066712\n",
      "[201]\tvalid_0's l1: 0.0571679\tvalid_0's l2: 0.00666813\n",
      "[202]\tvalid_0's l1: 0.0571538\tvalid_0's l2: 0.00666858\n",
      "[203]\tvalid_0's l1: 0.0571505\tvalid_0's l2: 0.00666737\n",
      "[204]\tvalid_0's l1: 0.0571496\tvalid_0's l2: 0.00666642\n",
      "[205]\tvalid_0's l1: 0.0571438\tvalid_0's l2: 0.00666369\n",
      "[206]\tvalid_0's l1: 0.0571522\tvalid_0's l2: 0.0066647\n",
      "[207]\tvalid_0's l1: 0.0571443\tvalid_0's l2: 0.00666353\n",
      "[208]\tvalid_0's l1: 0.057147\tvalid_0's l2: 0.00666341\n",
      "[209]\tvalid_0's l1: 0.0571413\tvalid_0's l2: 0.00666194\n",
      "[210]\tvalid_0's l1: 0.0571264\tvalid_0's l2: 0.00666014\n",
      "[211]\tvalid_0's l1: 0.0571122\tvalid_0's l2: 0.00665585\n",
      "[212]\tvalid_0's l1: 0.0571118\tvalid_0's l2: 0.00665518\n",
      "[213]\tvalid_0's l1: 0.0571002\tvalid_0's l2: 0.00665338\n",
      "[214]\tvalid_0's l1: 0.0570964\tvalid_0's l2: 0.00665241\n",
      "[215]\tvalid_0's l1: 0.05709\tvalid_0's l2: 0.00665055\n",
      "[216]\tvalid_0's l1: 0.0570856\tvalid_0's l2: 0.00665057\n",
      "[217]\tvalid_0's l1: 0.0570826\tvalid_0's l2: 0.00665004\n",
      "[218]\tvalid_0's l1: 0.0570747\tvalid_0's l2: 0.00664898\n",
      "[219]\tvalid_0's l1: 0.057068\tvalid_0's l2: 0.00664791\n",
      "[220]\tvalid_0's l1: 0.05707\tvalid_0's l2: 0.00664691\n",
      "[221]\tvalid_0's l1: 0.0570675\tvalid_0's l2: 0.00664523\n",
      "[222]\tvalid_0's l1: 0.0570426\tvalid_0's l2: 0.00664251\n",
      "[223]\tvalid_0's l1: 0.0570167\tvalid_0's l2: 0.00663508\n",
      "[224]\tvalid_0's l1: 0.0570101\tvalid_0's l2: 0.00663261\n",
      "[225]\tvalid_0's l1: 0.0570103\tvalid_0's l2: 0.00663457\n",
      "[226]\tvalid_0's l1: 0.0569972\tvalid_0's l2: 0.00663136\n",
      "[227]\tvalid_0's l1: 0.0569882\tvalid_0's l2: 0.00663009\n",
      "[228]\tvalid_0's l1: 0.0569898\tvalid_0's l2: 0.00662954\n",
      "[229]\tvalid_0's l1: 0.0569865\tvalid_0's l2: 0.00662705\n",
      "[230]\tvalid_0's l1: 0.0569757\tvalid_0's l2: 0.00662507\n",
      "[231]\tvalid_0's l1: 0.0569731\tvalid_0's l2: 0.00662383\n",
      "[232]\tvalid_0's l1: 0.0569676\tvalid_0's l2: 0.00662384\n",
      "[233]\tvalid_0's l1: 0.0569664\tvalid_0's l2: 0.00662459\n",
      "[234]\tvalid_0's l1: 0.0569611\tvalid_0's l2: 0.00662457\n",
      "[235]\tvalid_0's l1: 0.0569633\tvalid_0's l2: 0.00662413\n",
      "[236]\tvalid_0's l1: 0.0569615\tvalid_0's l2: 0.00662337\n",
      "[237]\tvalid_0's l1: 0.0569616\tvalid_0's l2: 0.00662352\n",
      "[238]\tvalid_0's l1: 0.0569541\tvalid_0's l2: 0.00662248\n",
      "[239]\tvalid_0's l1: 0.0569478\tvalid_0's l2: 0.0066219\n",
      "[240]\tvalid_0's l1: 0.0569487\tvalid_0's l2: 0.00662202\n",
      "[241]\tvalid_0's l1: 0.0569517\tvalid_0's l2: 0.00662222\n",
      "[242]\tvalid_0's l1: 0.0569443\tvalid_0's l2: 0.00662104\n",
      "[243]\tvalid_0's l1: 0.0569249\tvalid_0's l2: 0.00661558\n",
      "[244]\tvalid_0's l1: 0.0569245\tvalid_0's l2: 0.00661384\n",
      "[245]\tvalid_0's l1: 0.0569247\tvalid_0's l2: 0.00661414\n",
      "[246]\tvalid_0's l1: 0.0569229\tvalid_0's l2: 0.00661358\n",
      "[247]\tvalid_0's l1: 0.0569195\tvalid_0's l2: 0.00661233\n",
      "[248]\tvalid_0's l1: 0.0569077\tvalid_0's l2: 0.00660972\n",
      "[249]\tvalid_0's l1: 0.0569058\tvalid_0's l2: 0.00660907\n",
      "[250]\tvalid_0's l1: 0.0569022\tvalid_0's l2: 0.00660765\n",
      "[251]\tvalid_0's l1: 0.056903\tvalid_0's l2: 0.00660776\n",
      "[252]\tvalid_0's l1: 0.0569011\tvalid_0's l2: 0.00660744\n",
      "[253]\tvalid_0's l1: 0.0569056\tvalid_0's l2: 0.00660932\n",
      "[254]\tvalid_0's l1: 0.0569014\tvalid_0's l2: 0.00660959\n",
      "[255]\tvalid_0's l1: 0.0568977\tvalid_0's l2: 0.00660912\n",
      "[256]\tvalid_0's l1: 0.0568991\tvalid_0's l2: 0.00660947\n",
      "[257]\tvalid_0's l1: 0.0568659\tvalid_0's l2: 0.00660135\n",
      "[258]\tvalid_0's l1: 0.0568594\tvalid_0's l2: 0.00660015\n",
      "[259]\tvalid_0's l1: 0.0568588\tvalid_0's l2: 0.00659937\n",
      "[260]\tvalid_0's l1: 0.0568588\tvalid_0's l2: 0.00659942\n",
      "[261]\tvalid_0's l1: 0.056858\tvalid_0's l2: 0.00659948\n",
      "[262]\tvalid_0's l1: 0.0568521\tvalid_0's l2: 0.00659886\n",
      "[263]\tvalid_0's l1: 0.0568516\tvalid_0's l2: 0.00659866\n",
      "[264]\tvalid_0's l1: 0.0568237\tvalid_0's l2: 0.00659403\n",
      "[265]\tvalid_0's l1: 0.0568185\tvalid_0's l2: 0.00659247\n",
      "[266]\tvalid_0's l1: 0.0568207\tvalid_0's l2: 0.00659355\n",
      "[267]\tvalid_0's l1: 0.0568153\tvalid_0's l2: 0.00659291\n",
      "[268]\tvalid_0's l1: 0.0568104\tvalid_0's l2: 0.00659252\n",
      "[269]\tvalid_0's l1: 0.0568035\tvalid_0's l2: 0.00659123\n",
      "[270]\tvalid_0's l1: 0.0567872\tvalid_0's l2: 0.00658465\n",
      "[271]\tvalid_0's l1: 0.0567721\tvalid_0's l2: 0.00658252\n",
      "[272]\tvalid_0's l1: 0.0567615\tvalid_0's l2: 0.00658002\n",
      "[273]\tvalid_0's l1: 0.0567417\tvalid_0's l2: 0.00657648\n",
      "[274]\tvalid_0's l1: 0.0567421\tvalid_0's l2: 0.00657556\n",
      "[275]\tvalid_0's l1: 0.0567452\tvalid_0's l2: 0.00657642\n",
      "[276]\tvalid_0's l1: 0.0567469\tvalid_0's l2: 0.00657607\n",
      "[277]\tvalid_0's l1: 0.056743\tvalid_0's l2: 0.00657593\n",
      "[278]\tvalid_0's l1: 0.0567391\tvalid_0's l2: 0.00657495\n",
      "[279]\tvalid_0's l1: 0.056739\tvalid_0's l2: 0.00657478\n",
      "[280]\tvalid_0's l1: 0.0567325\tvalid_0's l2: 0.00657337\n",
      "[281]\tvalid_0's l1: 0.0567323\tvalid_0's l2: 0.00657334\n",
      "[282]\tvalid_0's l1: 0.0567366\tvalid_0's l2: 0.00657365\n",
      "[283]\tvalid_0's l1: 0.0567364\tvalid_0's l2: 0.00657325\n",
      "[284]\tvalid_0's l1: 0.0567233\tvalid_0's l2: 0.00657079\n",
      "[285]\tvalid_0's l1: 0.0567246\tvalid_0's l2: 0.0065718\n",
      "[286]\tvalid_0's l1: 0.0567265\tvalid_0's l2: 0.00657222\n",
      "[287]\tvalid_0's l1: 0.0567223\tvalid_0's l2: 0.00657141\n",
      "[288]\tvalid_0's l1: 0.056714\tvalid_0's l2: 0.00656963\n",
      "[289]\tvalid_0's l1: 0.0567092\tvalid_0's l2: 0.00656967\n",
      "[290]\tvalid_0's l1: 0.0566989\tvalid_0's l2: 0.00656775\n",
      "[291]\tvalid_0's l1: 0.0566969\tvalid_0's l2: 0.00656714\n",
      "[292]\tvalid_0's l1: 0.0566972\tvalid_0's l2: 0.00656797\n",
      "[293]\tvalid_0's l1: 0.0566986\tvalid_0's l2: 0.00656812\n",
      "[294]\tvalid_0's l1: 0.056693\tvalid_0's l2: 0.00656709\n",
      "[295]\tvalid_0's l1: 0.0566943\tvalid_0's l2: 0.00656743\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[296]\tvalid_0's l1: 0.0566987\tvalid_0's l2: 0.0065679\n",
      "[297]\tvalid_0's l1: 0.056698\tvalid_0's l2: 0.00656738\n",
      "[298]\tvalid_0's l1: 0.0566927\tvalid_0's l2: 0.00656645\n",
      "[299]\tvalid_0's l1: 0.0566939\tvalid_0's l2: 0.00656589\n",
      "[300]\tvalid_0's l1: 0.0566834\tvalid_0's l2: 0.00656346\n",
      "[301]\tvalid_0's l1: 0.0566872\tvalid_0's l2: 0.0065663\n",
      "[302]\tvalid_0's l1: 0.0566856\tvalid_0's l2: 0.00656487\n",
      "[303]\tvalid_0's l1: 0.0566822\tvalid_0's l2: 0.00656261\n",
      "[304]\tvalid_0's l1: 0.0566848\tvalid_0's l2: 0.00656279\n",
      "[305]\tvalid_0's l1: 0.0566818\tvalid_0's l2: 0.00656087\n",
      "[306]\tvalid_0's l1: 0.0566818\tvalid_0's l2: 0.00656023\n",
      "[307]\tvalid_0's l1: 0.0566825\tvalid_0's l2: 0.00655957\n",
      "[308]\tvalid_0's l1: 0.0566804\tvalid_0's l2: 0.00655989\n",
      "[309]\tvalid_0's l1: 0.0566693\tvalid_0's l2: 0.00655513\n",
      "[310]\tvalid_0's l1: 0.0566608\tvalid_0's l2: 0.00655397\n",
      "[311]\tvalid_0's l1: 0.0566546\tvalid_0's l2: 0.00655309\n",
      "[312]\tvalid_0's l1: 0.0566519\tvalid_0's l2: 0.00655274\n",
      "[313]\tvalid_0's l1: 0.05665\tvalid_0's l2: 0.00655275\n",
      "[314]\tvalid_0's l1: 0.0566492\tvalid_0's l2: 0.00655257\n",
      "[315]\tvalid_0's l1: 0.0566516\tvalid_0's l2: 0.00655293\n",
      "[316]\tvalid_0's l1: 0.0566499\tvalid_0's l2: 0.00655212\n",
      "[317]\tvalid_0's l1: 0.0566385\tvalid_0's l2: 0.00655\n",
      "[318]\tvalid_0's l1: 0.0566288\tvalid_0's l2: 0.00654956\n",
      "[319]\tvalid_0's l1: 0.0566317\tvalid_0's l2: 0.00655049\n",
      "[320]\tvalid_0's l1: 0.056631\tvalid_0's l2: 0.00655062\n",
      "[321]\tvalid_0's l1: 0.0566272\tvalid_0's l2: 0.00654966\n",
      "[322]\tvalid_0's l1: 0.0566247\tvalid_0's l2: 0.00654857\n",
      "[323]\tvalid_0's l1: 0.0566318\tvalid_0's l2: 0.00654932\n",
      "[324]\tvalid_0's l1: 0.0566344\tvalid_0's l2: 0.00655044\n",
      "[325]\tvalid_0's l1: 0.0566239\tvalid_0's l2: 0.00654858\n",
      "[326]\tvalid_0's l1: 0.0566241\tvalid_0's l2: 0.00654811\n",
      "[327]\tvalid_0's l1: 0.056621\tvalid_0's l2: 0.00654755\n",
      "[328]\tvalid_0's l1: 0.056627\tvalid_0's l2: 0.00655085\n",
      "[329]\tvalid_0's l1: 0.0566305\tvalid_0's l2: 0.00655121\n",
      "[330]\tvalid_0's l1: 0.0566281\tvalid_0's l2: 0.00655078\n",
      "[331]\tvalid_0's l1: 0.0566228\tvalid_0's l2: 0.00655045\n",
      "[332]\tvalid_0's l1: 0.0566242\tvalid_0's l2: 0.00654992\n",
      "Early stopping, best iteration is:\n",
      "[327]\tvalid_0's l1: 0.056621\tvalid_0's l2: 0.00654755\n",
      "本次结果输出的mae值是:\n",
      " 0.0566209902947507\n",
      "[1]\tvalid_0's l1: 0.244506\tvalid_0's l2: 0.0790859\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[2]\tvalid_0's l1: 0.223857\tvalid_0's l2: 0.0667341\n",
      "[3]\tvalid_0's l1: 0.205626\tvalid_0's l2: 0.0566941\n",
      "[4]\tvalid_0's l1: 0.189298\tvalid_0's l2: 0.0484539\n",
      "[5]\tvalid_0's l1: 0.175104\tvalid_0's l2: 0.0417921\n",
      "[6]\tvalid_0's l1: 0.16227\tvalid_0's l2: 0.036285\n",
      "[7]\tvalid_0's l1: 0.150898\tvalid_0's l2: 0.0317221\n",
      "[8]\tvalid_0's l1: 0.140883\tvalid_0's l2: 0.0280196\n",
      "[9]\tvalid_0's l1: 0.131939\tvalid_0's l2: 0.0249613\n",
      "[10]\tvalid_0's l1: 0.124375\tvalid_0's l2: 0.0224944\n",
      "[11]\tvalid_0's l1: 0.117467\tvalid_0's l2: 0.0204263\n",
      "[12]\tvalid_0's l1: 0.110957\tvalid_0's l2: 0.0185453\n",
      "[13]\tvalid_0's l1: 0.105077\tvalid_0's l2: 0.016929\n",
      "[14]\tvalid_0's l1: 0.100272\tvalid_0's l2: 0.0157152\n",
      "[15]\tvalid_0's l1: 0.0960833\tvalid_0's l2: 0.0147431\n",
      "[16]\tvalid_0's l1: 0.0920567\tvalid_0's l2: 0.0137979\n",
      "[17]\tvalid_0's l1: 0.088268\tvalid_0's l2: 0.0129121\n",
      "[18]\tvalid_0's l1: 0.0854499\tvalid_0's l2: 0.0123437\n",
      "[19]\tvalid_0's l1: 0.0827065\tvalid_0's l2: 0.0117755\n",
      "[20]\tvalid_0's l1: 0.0800967\tvalid_0's l2: 0.0112162\n",
      "[21]\tvalid_0's l1: 0.0781237\tvalid_0's l2: 0.0108399\n",
      "[22]\tvalid_0's l1: 0.0763776\tvalid_0's l2: 0.0105204\n",
      "[23]\tvalid_0's l1: 0.074906\tvalid_0's l2: 0.010265\n",
      "[24]\tvalid_0's l1: 0.0732985\tvalid_0's l2: 0.00994854\n",
      "[25]\tvalid_0's l1: 0.0720481\tvalid_0's l2: 0.00970346\n",
      "[26]\tvalid_0's l1: 0.0710837\tvalid_0's l2: 0.00953733\n",
      "[27]\tvalid_0's l1: 0.0703066\tvalid_0's l2: 0.00939487\n",
      "[28]\tvalid_0's l1: 0.0694075\tvalid_0's l2: 0.009231\n",
      "[29]\tvalid_0's l1: 0.0685866\tvalid_0's l2: 0.00907982\n",
      "[30]\tvalid_0's l1: 0.06779\tvalid_0's l2: 0.0089391\n",
      "[31]\tvalid_0's l1: 0.067074\tvalid_0's l2: 0.00880224\n",
      "[32]\tvalid_0's l1: 0.0665149\tvalid_0's l2: 0.00871073\n",
      "[33]\tvalid_0's l1: 0.0659205\tvalid_0's l2: 0.00858992\n",
      "[34]\tvalid_0's l1: 0.0652426\tvalid_0's l2: 0.00843778\n",
      "[35]\tvalid_0's l1: 0.0648768\tvalid_0's l2: 0.00836372\n",
      "[36]\tvalid_0's l1: 0.0645306\tvalid_0's l2: 0.00830614\n",
      "[37]\tvalid_0's l1: 0.0640175\tvalid_0's l2: 0.00818645\n",
      "[38]\tvalid_0's l1: 0.0637947\tvalid_0's l2: 0.00814636\n",
      "[39]\tvalid_0's l1: 0.0635605\tvalid_0's l2: 0.00810447\n",
      "[40]\tvalid_0's l1: 0.0633449\tvalid_0's l2: 0.00806796\n",
      "[41]\tvalid_0's l1: 0.0631446\tvalid_0's l2: 0.00802436\n",
      "[42]\tvalid_0's l1: 0.0627575\tvalid_0's l2: 0.00792715\n",
      "[43]\tvalid_0's l1: 0.062611\tvalid_0's l2: 0.00789809\n",
      "[44]\tvalid_0's l1: 0.0623997\tvalid_0's l2: 0.00785422\n",
      "[45]\tvalid_0's l1: 0.0622728\tvalid_0's l2: 0.00783369\n",
      "[46]\tvalid_0's l1: 0.0621336\tvalid_0's l2: 0.00781042\n",
      "[47]\tvalid_0's l1: 0.0619906\tvalid_0's l2: 0.00777463\n",
      "[48]\tvalid_0's l1: 0.0618556\tvalid_0's l2: 0.00774471\n",
      "[49]\tvalid_0's l1: 0.0617449\tvalid_0's l2: 0.00772713\n",
      "[50]\tvalid_0's l1: 0.0616454\tvalid_0's l2: 0.00770345\n",
      "[51]\tvalid_0's l1: 0.0615492\tvalid_0's l2: 0.00768433\n",
      "[52]\tvalid_0's l1: 0.0614452\tvalid_0's l2: 0.00766572\n",
      "[53]\tvalid_0's l1: 0.0613725\tvalid_0's l2: 0.00764408\n",
      "[54]\tvalid_0's l1: 0.0612796\tvalid_0's l2: 0.00762834\n",
      "[55]\tvalid_0's l1: 0.0611706\tvalid_0's l2: 0.00760066\n",
      "[56]\tvalid_0's l1: 0.0611121\tvalid_0's l2: 0.00759012\n",
      "[57]\tvalid_0's l1: 0.0610472\tvalid_0's l2: 0.00757864\n",
      "[58]\tvalid_0's l1: 0.0609801\tvalid_0's l2: 0.00756664\n",
      "[59]\tvalid_0's l1: 0.0608993\tvalid_0's l2: 0.00755207\n",
      "[60]\tvalid_0's l1: 0.0608206\tvalid_0's l2: 0.00753343\n",
      "[61]\tvalid_0's l1: 0.0607751\tvalid_0's l2: 0.00752435\n",
      "[62]\tvalid_0's l1: 0.0607163\tvalid_0's l2: 0.00751128\n",
      "[63]\tvalid_0's l1: 0.0605746\tvalid_0's l2: 0.00747752\n",
      "[64]\tvalid_0's l1: 0.0604651\tvalid_0's l2: 0.0074507\n",
      "[65]\tvalid_0's l1: 0.0604263\tvalid_0's l2: 0.00744162\n",
      "[66]\tvalid_0's l1: 0.0603599\tvalid_0's l2: 0.00742889\n",
      "[67]\tvalid_0's l1: 0.0602701\tvalid_0's l2: 0.00741169\n",
      "[68]\tvalid_0's l1: 0.0602334\tvalid_0's l2: 0.00739977\n",
      "[69]\tvalid_0's l1: 0.060112\tvalid_0's l2: 0.007374\n",
      "[70]\tvalid_0's l1: 0.0600257\tvalid_0's l2: 0.00735234\n",
      "[71]\tvalid_0's l1: 0.059928\tvalid_0's l2: 0.00732775\n",
      "[72]\tvalid_0's l1: 0.0598568\tvalid_0's l2: 0.00731406\n",
      "[73]\tvalid_0's l1: 0.0598019\tvalid_0's l2: 0.007307\n",
      "[74]\tvalid_0's l1: 0.0597175\tvalid_0's l2: 0.00728248\n",
      "[75]\tvalid_0's l1: 0.0596246\tvalid_0's l2: 0.00726385\n",
      "[76]\tvalid_0's l1: 0.0596029\tvalid_0's l2: 0.00725546\n",
      "[77]\tvalid_0's l1: 0.0595231\tvalid_0's l2: 0.00723235\n",
      "[78]\tvalid_0's l1: 0.0595014\tvalid_0's l2: 0.00722831\n",
      "[79]\tvalid_0's l1: 0.0594712\tvalid_0's l2: 0.00722155\n",
      "[80]\tvalid_0's l1: 0.0594512\tvalid_0's l2: 0.00721585\n",
      "[81]\tvalid_0's l1: 0.0594197\tvalid_0's l2: 0.00720856\n",
      "[82]\tvalid_0's l1: 0.0593932\tvalid_0's l2: 0.00720475\n",
      "[83]\tvalid_0's l1: 0.0593157\tvalid_0's l2: 0.00718559\n",
      "[84]\tvalid_0's l1: 0.0592926\tvalid_0's l2: 0.00718174\n",
      "[85]\tvalid_0's l1: 0.0592627\tvalid_0's l2: 0.00717558\n",
      "[86]\tvalid_0's l1: 0.0592466\tvalid_0's l2: 0.00716984\n",
      "[87]\tvalid_0's l1: 0.0592043\tvalid_0's l2: 0.00716193\n",
      "[88]\tvalid_0's l1: 0.0591298\tvalid_0's l2: 0.00714342\n",
      "[89]\tvalid_0's l1: 0.059097\tvalid_0's l2: 0.00713607\n",
      "[90]\tvalid_0's l1: 0.0589982\tvalid_0's l2: 0.00711745\n",
      "[91]\tvalid_0's l1: 0.0589505\tvalid_0's l2: 0.00710413\n",
      "[92]\tvalid_0's l1: 0.0588884\tvalid_0's l2: 0.00708708\n",
      "[93]\tvalid_0's l1: 0.0588084\tvalid_0's l2: 0.00707062\n",
      "[94]\tvalid_0's l1: 0.0587967\tvalid_0's l2: 0.00707269\n",
      "[95]\tvalid_0's l1: 0.0587689\tvalid_0's l2: 0.00706628\n",
      "[96]\tvalid_0's l1: 0.058749\tvalid_0's l2: 0.00705746\n",
      "[97]\tvalid_0's l1: 0.0587121\tvalid_0's l2: 0.00704977\n",
      "[98]\tvalid_0's l1: 0.0586712\tvalid_0's l2: 0.00703874\n",
      "[99]\tvalid_0's l1: 0.05861\tvalid_0's l2: 0.0070237\n",
      "[100]\tvalid_0's l1: 0.0585889\tvalid_0's l2: 0.00701449\n",
      "[101]\tvalid_0's l1: 0.0585771\tvalid_0's l2: 0.00701003\n",
      "[102]\tvalid_0's l1: 0.0585635\tvalid_0's l2: 0.0070058\n",
      "[103]\tvalid_0's l1: 0.0585479\tvalid_0's l2: 0.00700123\n",
      "[104]\tvalid_0's l1: 0.0585384\tvalid_0's l2: 0.00699934\n",
      "[105]\tvalid_0's l1: 0.0585209\tvalid_0's l2: 0.00699689\n",
      "[106]\tvalid_0's l1: 0.0585063\tvalid_0's l2: 0.00699365\n",
      "[107]\tvalid_0's l1: 0.0584905\tvalid_0's l2: 0.00698833\n",
      "[108]\tvalid_0's l1: 0.0584738\tvalid_0's l2: 0.00698361\n",
      "[109]\tvalid_0's l1: 0.0584563\tvalid_0's l2: 0.00697852\n",
      "[110]\tvalid_0's l1: 0.0584379\tvalid_0's l2: 0.00697721\n",
      "[111]\tvalid_0's l1: 0.0584333\tvalid_0's l2: 0.00697385\n",
      "[112]\tvalid_0's l1: 0.0584316\tvalid_0's l2: 0.00697371\n",
      "[113]\tvalid_0's l1: 0.0584076\tvalid_0's l2: 0.00696515\n",
      "[114]\tvalid_0's l1: 0.0583903\tvalid_0's l2: 0.00696145\n",
      "[115]\tvalid_0's l1: 0.0583817\tvalid_0's l2: 0.00695705\n",
      "[116]\tvalid_0's l1: 0.0583585\tvalid_0's l2: 0.00695171\n",
      "[117]\tvalid_0's l1: 0.0583329\tvalid_0's l2: 0.00694439\n",
      "[118]\tvalid_0's l1: 0.0582953\tvalid_0's l2: 0.00693451\n",
      "[119]\tvalid_0's l1: 0.0582302\tvalid_0's l2: 0.00691471\n",
      "[120]\tvalid_0's l1: 0.0582053\tvalid_0's l2: 0.00690717\n",
      "[121]\tvalid_0's l1: 0.058165\tvalid_0's l2: 0.00690191\n",
      "[122]\tvalid_0's l1: 0.0581507\tvalid_0's l2: 0.00689919\n",
      "[123]\tvalid_0's l1: 0.0581202\tvalid_0's l2: 0.00689344\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[124]\tvalid_0's l1: 0.0580842\tvalid_0's l2: 0.00688742\n",
      "[125]\tvalid_0's l1: 0.0580695\tvalid_0's l2: 0.00688381\n",
      "[126]\tvalid_0's l1: 0.0580556\tvalid_0's l2: 0.0068812\n",
      "[127]\tvalid_0's l1: 0.0580404\tvalid_0's l2: 0.00687439\n",
      "[128]\tvalid_0's l1: 0.0580276\tvalid_0's l2: 0.00687128\n",
      "[129]\tvalid_0's l1: 0.0579982\tvalid_0's l2: 0.00686654\n",
      "[130]\tvalid_0's l1: 0.0579886\tvalid_0's l2: 0.00686163\n",
      "[131]\tvalid_0's l1: 0.0579795\tvalid_0's l2: 0.00686265\n",
      "[132]\tvalid_0's l1: 0.0579773\tvalid_0's l2: 0.00686113\n",
      "[133]\tvalid_0's l1: 0.0579718\tvalid_0's l2: 0.00686023\n",
      "[134]\tvalid_0's l1: 0.0579558\tvalid_0's l2: 0.00685794\n",
      "[135]\tvalid_0's l1: 0.0579452\tvalid_0's l2: 0.00685478\n",
      "[136]\tvalid_0's l1: 0.0579375\tvalid_0's l2: 0.00685219\n",
      "[137]\tvalid_0's l1: 0.0579311\tvalid_0's l2: 0.00685004\n",
      "[138]\tvalid_0's l1: 0.0578929\tvalid_0's l2: 0.0068395\n",
      "[139]\tvalid_0's l1: 0.0578863\tvalid_0's l2: 0.00683737\n",
      "[140]\tvalid_0's l1: 0.0578618\tvalid_0's l2: 0.00682973\n",
      "[141]\tvalid_0's l1: 0.057814\tvalid_0's l2: 0.00682006\n",
      "[142]\tvalid_0's l1: 0.0577865\tvalid_0's l2: 0.00681178\n",
      "[143]\tvalid_0's l1: 0.0577836\tvalid_0's l2: 0.00681092\n",
      "[144]\tvalid_0's l1: 0.0577732\tvalid_0's l2: 0.00680807\n",
      "[145]\tvalid_0's l1: 0.05775\tvalid_0's l2: 0.00680476\n",
      "[146]\tvalid_0's l1: 0.0577295\tvalid_0's l2: 0.00679983\n",
      "[147]\tvalid_0's l1: 0.0577274\tvalid_0's l2: 0.00679906\n",
      "[148]\tvalid_0's l1: 0.0577282\tvalid_0's l2: 0.00680071\n",
      "[149]\tvalid_0's l1: 0.0577027\tvalid_0's l2: 0.00679154\n",
      "[150]\tvalid_0's l1: 0.0577023\tvalid_0's l2: 0.00679031\n",
      "[151]\tvalid_0's l1: 0.0576895\tvalid_0's l2: 0.0067869\n",
      "[152]\tvalid_0's l1: 0.0576863\tvalid_0's l2: 0.00678669\n",
      "[153]\tvalid_0's l1: 0.057681\tvalid_0's l2: 0.00678298\n",
      "[154]\tvalid_0's l1: 0.0576461\tvalid_0's l2: 0.00677587\n",
      "[155]\tvalid_0's l1: 0.0576281\tvalid_0's l2: 0.00676922\n",
      "[156]\tvalid_0's l1: 0.0576071\tvalid_0's l2: 0.00676582\n",
      "[157]\tvalid_0's l1: 0.0575831\tvalid_0's l2: 0.00676433\n",
      "[158]\tvalid_0's l1: 0.0575486\tvalid_0's l2: 0.00675594\n",
      "[159]\tvalid_0's l1: 0.0575371\tvalid_0's l2: 0.00675428\n",
      "[160]\tvalid_0's l1: 0.0575191\tvalid_0's l2: 0.00675119\n",
      "[161]\tvalid_0's l1: 0.0575124\tvalid_0's l2: 0.00675132\n",
      "[162]\tvalid_0's l1: 0.0575015\tvalid_0's l2: 0.00674851\n",
      "[163]\tvalid_0's l1: 0.0574785\tvalid_0's l2: 0.00674612\n",
      "[164]\tvalid_0's l1: 0.0574678\tvalid_0's l2: 0.00674357\n",
      "[165]\tvalid_0's l1: 0.0574633\tvalid_0's l2: 0.00674366\n",
      "[166]\tvalid_0's l1: 0.0574458\tvalid_0's l2: 0.00673905\n",
      "[167]\tvalid_0's l1: 0.0574112\tvalid_0's l2: 0.00672946\n",
      "[168]\tvalid_0's l1: 0.0573985\tvalid_0's l2: 0.00672684\n",
      "[169]\tvalid_0's l1: 0.0573967\tvalid_0's l2: 0.00672339\n",
      "[170]\tvalid_0's l1: 0.0573916\tvalid_0's l2: 0.00672143\n",
      "[171]\tvalid_0's l1: 0.0573821\tvalid_0's l2: 0.00671896\n",
      "[172]\tvalid_0's l1: 0.0573703\tvalid_0's l2: 0.00671675\n",
      "[173]\tvalid_0's l1: 0.0573778\tvalid_0's l2: 0.00671835\n",
      "[174]\tvalid_0's l1: 0.0573707\tvalid_0's l2: 0.00671666\n",
      "[175]\tvalid_0's l1: 0.0573572\tvalid_0's l2: 0.00671479\n",
      "[176]\tvalid_0's l1: 0.0573574\tvalid_0's l2: 0.0067135\n",
      "[177]\tvalid_0's l1: 0.0573542\tvalid_0's l2: 0.00671297\n",
      "[178]\tvalid_0's l1: 0.057355\tvalid_0's l2: 0.00671458\n",
      "[179]\tvalid_0's l1: 0.0573403\tvalid_0's l2: 0.00671148\n",
      "[180]\tvalid_0's l1: 0.0573278\tvalid_0's l2: 0.00670699\n",
      "[181]\tvalid_0's l1: 0.0573277\tvalid_0's l2: 0.00670656\n",
      "[182]\tvalid_0's l1: 0.0573192\tvalid_0's l2: 0.00670432\n",
      "[183]\tvalid_0's l1: 0.0573012\tvalid_0's l2: 0.00670044\n",
      "[184]\tvalid_0's l1: 0.0573018\tvalid_0's l2: 0.00669917\n",
      "[185]\tvalid_0's l1: 0.0572933\tvalid_0's l2: 0.00669791\n",
      "[186]\tvalid_0's l1: 0.0572937\tvalid_0's l2: 0.00669628\n",
      "[187]\tvalid_0's l1: 0.0572959\tvalid_0's l2: 0.006696\n",
      "[188]\tvalid_0's l1: 0.0572946\tvalid_0's l2: 0.00669529\n",
      "[189]\tvalid_0's l1: 0.057284\tvalid_0's l2: 0.00669419\n",
      "[190]\tvalid_0's l1: 0.0572756\tvalid_0's l2: 0.00669183\n",
      "[191]\tvalid_0's l1: 0.0572536\tvalid_0's l2: 0.00668716\n",
      "[192]\tvalid_0's l1: 0.0572549\tvalid_0's l2: 0.0066889\n",
      "[193]\tvalid_0's l1: 0.0572407\tvalid_0's l2: 0.00668565\n",
      "[194]\tvalid_0's l1: 0.0572137\tvalid_0's l2: 0.00668092\n",
      "[195]\tvalid_0's l1: 0.0572176\tvalid_0's l2: 0.00668088\n",
      "[196]\tvalid_0's l1: 0.0571987\tvalid_0's l2: 0.00667881\n",
      "[197]\tvalid_0's l1: 0.0571904\tvalid_0's l2: 0.00667394\n",
      "[198]\tvalid_0's l1: 0.0571825\tvalid_0's l2: 0.00667086\n",
      "[199]\tvalid_0's l1: 0.0571814\tvalid_0's l2: 0.0066715\n",
      "[200]\tvalid_0's l1: 0.0571754\tvalid_0's l2: 0.0066712\n",
      "[201]\tvalid_0's l1: 0.0571679\tvalid_0's l2: 0.00666813\n",
      "[202]\tvalid_0's l1: 0.0571538\tvalid_0's l2: 0.00666858\n",
      "[203]\tvalid_0's l1: 0.0571505\tvalid_0's l2: 0.00666737\n",
      "[204]\tvalid_0's l1: 0.0571496\tvalid_0's l2: 0.00666642\n",
      "[205]\tvalid_0's l1: 0.0571438\tvalid_0's l2: 0.00666369\n",
      "[206]\tvalid_0's l1: 0.0571522\tvalid_0's l2: 0.0066647\n",
      "[207]\tvalid_0's l1: 0.0571443\tvalid_0's l2: 0.00666353\n",
      "[208]\tvalid_0's l1: 0.057147\tvalid_0's l2: 0.00666341\n",
      "[209]\tvalid_0's l1: 0.0571413\tvalid_0's l2: 0.00666194\n",
      "[210]\tvalid_0's l1: 0.0571264\tvalid_0's l2: 0.00666014\n",
      "[211]\tvalid_0's l1: 0.0571122\tvalid_0's l2: 0.00665585\n",
      "[212]\tvalid_0's l1: 0.0571118\tvalid_0's l2: 0.00665518\n",
      "[213]\tvalid_0's l1: 0.0571002\tvalid_0's l2: 0.00665338\n",
      "[214]\tvalid_0's l1: 0.0570964\tvalid_0's l2: 0.00665241\n",
      "[215]\tvalid_0's l1: 0.05709\tvalid_0's l2: 0.00665055\n",
      "[216]\tvalid_0's l1: 0.0570856\tvalid_0's l2: 0.00665057\n",
      "[217]\tvalid_0's l1: 0.0570826\tvalid_0's l2: 0.00665004\n",
      "[218]\tvalid_0's l1: 0.0570747\tvalid_0's l2: 0.00664898\n",
      "[219]\tvalid_0's l1: 0.057068\tvalid_0's l2: 0.00664791\n",
      "[220]\tvalid_0's l1: 0.05707\tvalid_0's l2: 0.00664691\n",
      "[221]\tvalid_0's l1: 0.0570675\tvalid_0's l2: 0.00664523\n",
      "[222]\tvalid_0's l1: 0.0570426\tvalid_0's l2: 0.00664251\n",
      "[223]\tvalid_0's l1: 0.0570167\tvalid_0's l2: 0.00663508\n",
      "[224]\tvalid_0's l1: 0.0570101\tvalid_0's l2: 0.00663261\n",
      "[225]\tvalid_0's l1: 0.0570103\tvalid_0's l2: 0.00663457\n",
      "[226]\tvalid_0's l1: 0.0569972\tvalid_0's l2: 0.00663136\n",
      "[227]\tvalid_0's l1: 0.0569882\tvalid_0's l2: 0.00663009\n",
      "[228]\tvalid_0's l1: 0.0569898\tvalid_0's l2: 0.00662954\n",
      "[229]\tvalid_0's l1: 0.0569865\tvalid_0's l2: 0.00662705\n",
      "[230]\tvalid_0's l1: 0.0569757\tvalid_0's l2: 0.00662507\n",
      "[231]\tvalid_0's l1: 0.0569731\tvalid_0's l2: 0.00662383\n",
      "[232]\tvalid_0's l1: 0.0569676\tvalid_0's l2: 0.00662384\n",
      "[233]\tvalid_0's l1: 0.0569664\tvalid_0's l2: 0.00662459\n",
      "[234]\tvalid_0's l1: 0.0569611\tvalid_0's l2: 0.00662457\n",
      "[235]\tvalid_0's l1: 0.0569633\tvalid_0's l2: 0.00662413\n",
      "[236]\tvalid_0's l1: 0.0569615\tvalid_0's l2: 0.00662337\n",
      "[237]\tvalid_0's l1: 0.0569616\tvalid_0's l2: 0.00662352\n",
      "[238]\tvalid_0's l1: 0.0569541\tvalid_0's l2: 0.00662248\n",
      "[239]\tvalid_0's l1: 0.0569478\tvalid_0's l2: 0.0066219\n",
      "[240]\tvalid_0's l1: 0.0569487\tvalid_0's l2: 0.00662202\n",
      "[241]\tvalid_0's l1: 0.0569517\tvalid_0's l2: 0.00662222\n",
      "[242]\tvalid_0's l1: 0.0569443\tvalid_0's l2: 0.00662104\n",
      "[243]\tvalid_0's l1: 0.0569249\tvalid_0's l2: 0.00661558\n",
      "[244]\tvalid_0's l1: 0.0569245\tvalid_0's l2: 0.00661384\n",
      "[245]\tvalid_0's l1: 0.0569247\tvalid_0's l2: 0.00661414\n",
      "[246]\tvalid_0's l1: 0.0569229\tvalid_0's l2: 0.00661358\n",
      "[247]\tvalid_0's l1: 0.0569195\tvalid_0's l2: 0.00661233\n",
      "[248]\tvalid_0's l1: 0.0569077\tvalid_0's l2: 0.00660972\n",
      "[249]\tvalid_0's l1: 0.0569058\tvalid_0's l2: 0.00660907\n",
      "[250]\tvalid_0's l1: 0.0569022\tvalid_0's l2: 0.00660765\n",
      "[251]\tvalid_0's l1: 0.056903\tvalid_0's l2: 0.00660776\n",
      "[252]\tvalid_0's l1: 0.0569011\tvalid_0's l2: 0.00660744\n",
      "[253]\tvalid_0's l1: 0.0569056\tvalid_0's l2: 0.00660932\n",
      "[254]\tvalid_0's l1: 0.0569014\tvalid_0's l2: 0.00660959\n",
      "[255]\tvalid_0's l1: 0.0568977\tvalid_0's l2: 0.00660912\n",
      "[256]\tvalid_0's l1: 0.0568991\tvalid_0's l2: 0.00660947\n",
      "[257]\tvalid_0's l1: 0.0568659\tvalid_0's l2: 0.00660135\n",
      "[258]\tvalid_0's l1: 0.0568594\tvalid_0's l2: 0.00660015\n",
      "[259]\tvalid_0's l1: 0.0568588\tvalid_0's l2: 0.00659937\n",
      "[260]\tvalid_0's l1: 0.0568588\tvalid_0's l2: 0.00659942\n",
      "[261]\tvalid_0's l1: 0.056858\tvalid_0's l2: 0.00659948\n",
      "[262]\tvalid_0's l1: 0.0568521\tvalid_0's l2: 0.00659886\n",
      "[263]\tvalid_0's l1: 0.0568516\tvalid_0's l2: 0.00659866\n",
      "[264]\tvalid_0's l1: 0.0568237\tvalid_0's l2: 0.00659403\n",
      "[265]\tvalid_0's l1: 0.0568185\tvalid_0's l2: 0.00659247\n",
      "[266]\tvalid_0's l1: 0.0568207\tvalid_0's l2: 0.00659355\n",
      "[267]\tvalid_0's l1: 0.0568153\tvalid_0's l2: 0.00659291\n",
      "[268]\tvalid_0's l1: 0.0568104\tvalid_0's l2: 0.00659252\n",
      "[269]\tvalid_0's l1: 0.0568035\tvalid_0's l2: 0.00659123\n",
      "[270]\tvalid_0's l1: 0.0567872\tvalid_0's l2: 0.00658465\n",
      "[271]\tvalid_0's l1: 0.0567721\tvalid_0's l2: 0.00658252\n",
      "[272]\tvalid_0's l1: 0.0567615\tvalid_0's l2: 0.00658002\n",
      "[273]\tvalid_0's l1: 0.0567417\tvalid_0's l2: 0.00657648\n",
      "[274]\tvalid_0's l1: 0.0567421\tvalid_0's l2: 0.00657556\n",
      "[275]\tvalid_0's l1: 0.0567452\tvalid_0's l2: 0.00657642\n",
      "[276]\tvalid_0's l1: 0.0567469\tvalid_0's l2: 0.00657607\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[277]\tvalid_0's l1: 0.056743\tvalid_0's l2: 0.00657593\n",
      "[278]\tvalid_0's l1: 0.0567391\tvalid_0's l2: 0.00657495\n",
      "[279]\tvalid_0's l1: 0.056739\tvalid_0's l2: 0.00657478\n",
      "[280]\tvalid_0's l1: 0.0567325\tvalid_0's l2: 0.00657337\n",
      "[281]\tvalid_0's l1: 0.0567323\tvalid_0's l2: 0.00657334\n",
      "[282]\tvalid_0's l1: 0.0567366\tvalid_0's l2: 0.00657365\n",
      "[283]\tvalid_0's l1: 0.0567364\tvalid_0's l2: 0.00657325\n",
      "[284]\tvalid_0's l1: 0.0567233\tvalid_0's l2: 0.00657079\n",
      "[285]\tvalid_0's l1: 0.0567246\tvalid_0's l2: 0.0065718\n",
      "[286]\tvalid_0's l1: 0.0567265\tvalid_0's l2: 0.00657222\n",
      "[287]\tvalid_0's l1: 0.0567223\tvalid_0's l2: 0.00657141\n",
      "[288]\tvalid_0's l1: 0.056714\tvalid_0's l2: 0.00656963\n",
      "[289]\tvalid_0's l1: 0.0567092\tvalid_0's l2: 0.00656967\n",
      "[290]\tvalid_0's l1: 0.0566989\tvalid_0's l2: 0.00656775\n",
      "[291]\tvalid_0's l1: 0.0566969\tvalid_0's l2: 0.00656714\n",
      "[292]\tvalid_0's l1: 0.0566972\tvalid_0's l2: 0.00656797\n",
      "[293]\tvalid_0's l1: 0.0566986\tvalid_0's l2: 0.00656812\n",
      "[294]\tvalid_0's l1: 0.056693\tvalid_0's l2: 0.00656709\n",
      "[295]\tvalid_0's l1: 0.0566943\tvalid_0's l2: 0.00656743\n",
      "[296]\tvalid_0's l1: 0.0566987\tvalid_0's l2: 0.0065679\n",
      "[297]\tvalid_0's l1: 0.056698\tvalid_0's l2: 0.00656738\n",
      "[298]\tvalid_0's l1: 0.0566927\tvalid_0's l2: 0.00656645\n",
      "[299]\tvalid_0's l1: 0.0566939\tvalid_0's l2: 0.00656589\n",
      "[300]\tvalid_0's l1: 0.0566834\tvalid_0's l2: 0.00656346\n",
      "[301]\tvalid_0's l1: 0.0566872\tvalid_0's l2: 0.0065663\n",
      "[302]\tvalid_0's l1: 0.0566856\tvalid_0's l2: 0.00656487\n",
      "[303]\tvalid_0's l1: 0.0566822\tvalid_0's l2: 0.00656261\n",
      "[304]\tvalid_0's l1: 0.0566848\tvalid_0's l2: 0.00656279\n",
      "[305]\tvalid_0's l1: 0.0566818\tvalid_0's l2: 0.00656087\n",
      "[306]\tvalid_0's l1: 0.0566818\tvalid_0's l2: 0.00656023\n",
      "[307]\tvalid_0's l1: 0.0566825\tvalid_0's l2: 0.00655957\n",
      "[308]\tvalid_0's l1: 0.0566804\tvalid_0's l2: 0.00655989\n",
      "[309]\tvalid_0's l1: 0.0566693\tvalid_0's l2: 0.00655513\n",
      "[310]\tvalid_0's l1: 0.0566608\tvalid_0's l2: 0.00655397\n",
      "[311]\tvalid_0's l1: 0.0566546\tvalid_0's l2: 0.00655309\n",
      "[312]\tvalid_0's l1: 0.0566519\tvalid_0's l2: 0.00655274\n",
      "[313]\tvalid_0's l1: 0.05665\tvalid_0's l2: 0.00655275\n",
      "[314]\tvalid_0's l1: 0.0566492\tvalid_0's l2: 0.00655257\n",
      "[315]\tvalid_0's l1: 0.0566516\tvalid_0's l2: 0.00655293\n",
      "[316]\tvalid_0's l1: 0.0566499\tvalid_0's l2: 0.00655212\n",
      "[317]\tvalid_0's l1: 0.0566385\tvalid_0's l2: 0.00655\n",
      "[318]\tvalid_0's l1: 0.0566288\tvalid_0's l2: 0.00654956\n",
      "[319]\tvalid_0's l1: 0.0566317\tvalid_0's l2: 0.00655049\n",
      "[320]\tvalid_0's l1: 0.056631\tvalid_0's l2: 0.00655062\n",
      "[321]\tvalid_0's l1: 0.0566272\tvalid_0's l2: 0.00654966\n",
      "[322]\tvalid_0's l1: 0.0566247\tvalid_0's l2: 0.00654857\n",
      "[323]\tvalid_0's l1: 0.0566318\tvalid_0's l2: 0.00654932\n",
      "[324]\tvalid_0's l1: 0.0566344\tvalid_0's l2: 0.00655044\n",
      "[325]\tvalid_0's l1: 0.0566239\tvalid_0's l2: 0.00654858\n",
      "[326]\tvalid_0's l1: 0.0566241\tvalid_0's l2: 0.00654811\n",
      "[327]\tvalid_0's l1: 0.056621\tvalid_0's l2: 0.00654755\n",
      "[328]\tvalid_0's l1: 0.056627\tvalid_0's l2: 0.00655085\n",
      "[329]\tvalid_0's l1: 0.0566305\tvalid_0's l2: 0.00655121\n",
      "[330]\tvalid_0's l1: 0.0566281\tvalid_0's l2: 0.00655078\n",
      "[331]\tvalid_0's l1: 0.0566228\tvalid_0's l2: 0.00655045\n",
      "[332]\tvalid_0's l1: 0.0566242\tvalid_0's l2: 0.00654992\n",
      "Early stopping, best iteration is:\n",
      "[327]\tvalid_0's l1: 0.056621\tvalid_0's l2: 0.00654755\n",
      "本次结果输出的mae值是:\n",
      " 0.0566209902947507\n"
     ]
    }
   ],
   "source": [
    "# n_estimators\n",
    "\n",
    "scores = []\n",
    "n_estimators = [100, 300, 500, 800]\n",
    "\n",
    "for nes in  n_estimators:\n",
    "    lgbm = lgb.LGBMRegressor(boosting_type='gbdt', \n",
    "                      num_leaves=31,\n",
    "                      max_depth=5,\n",
    "                      learning_rate=0.1,\n",
    "                      n_estimators=nes,\n",
    "                      min_child_samples=20,\n",
    "                      n_jobs=-1)\n",
    "    \n",
    "    lgbm.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], eval_metric=\"l1\", early_stopping_rounds=5)\n",
    "    \n",
    "    y_pre = lgbm.predict(X_valid)\n",
    "    \n",
    "    mae = mean_absolute_error(y_valid, y_pre)\n",
    "    \n",
    "    scores.append(mae)\n",
    "    print(\"本次结果输出的mae值是:\\n\", mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best n_estimator 500\n"
     ]
    },
    {
     "data": {
      "image/png": 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9BktEvAF8GfgT4OvAxHwXZdmQxMKZk9j823d45lcDq6OzmRWvXC7efxv4AfAIyQX8r+a7KMvOvCnjOHX4YN8waWb9JpdTYR8GLgdeIvkSLV8JLiFJm5eJrNriNi9m1j9yCZZdJB8zDuAzJN8AaSXkWrd5MbN+lEuw/JLky7QagS8A5+S1Istc1zYv29s7Cl2OmQ1wuQTLFODKiPhgRDRHRGW+i7LsLWhKPnNx78pXClyJmQ10uQTLYeA/JS2T9JSkZb1uYUVnfNrm5btu82JmeZZLsLwBXBYRcyLi0oiY09NASTWSlkhaL+n+9Pvpex0jaaqkVkkr08dkSbO7PH9N0g09jOt1n5ZYODNp8/LdNW7zYmb5k0uwjAPuS2csy3qZsVwPtEbEFKCe5KJ/LmPqgbsioil9bI6IpzufAxuAdd2Ny3GfBpw3vo4ZZ41ymxczy6tcbpC8NJ2tHHkcZ/gc4Il0eRlwaY5j6oFrJK2R9GDXWYekIcBZEbGhh3G57NNSC2dO4re73ebFzPIn6xb4o0g+ngywGxiZ45gtwG0RMQ0YS3K/TKe5wNJ0ubtxuezTUrPObmDyqW7zYmb5k3Ww7ATq0uW69HkuY7YBT6brtgFjuoyfByzp8rNjx+WyTyQtktQiqaWtrXzbm0hiYXPS5mX5S92+VWZmJyXrYFkKXJYuzwGeynHMzcD89EvEzgNeAEhPdc0mOcVFD+Ny2ScRsTgiGiOisaGh4USPb0C4Km3zsnj51kKXYmYDUNbB8gAwXtIGkvb6WyXd0cuYpcCdwAJgNfBQRGxKx04FNkXE3vR5d+O6ez07Drd5MbN8UjmeZ29sbIyWlpZCl1FQuzoOMP0flzL3I6fy9fkXFbocMysBktZGRGNv4/z99WWqrraa+dPO5Edu82JmGXOwlLGb3ObFzPLAwVLG3ObFzPLBwVLm3ObFzLLmYClz542vY/oH3ebFzLLjYDEWNSdtXn7kNi9mlgEHi/2uzcsKt3kxs5PnYDEk8cczJ/LL37jNi5mdPAeLAXD1heM5dfhg7l7+cqFLMbMS52AxIGnzcuP0iazcspNf7HCbFzM7cQ4WO+Lai8/klEGVnrWY2UlxsNgRXdu87HCbFzM7QQ4WO8qCGRMAuHeV27yY2YlxsNhRTq8fwifPH8t317zG7r1u82JmfedgsfdZ1DyJd/cd5Lur3ebFzPrOwWLv87s2L9vc5sXM+szBYt1a2DyJ3+ze6zYvZtZnDhbr1my3eTGzE5RpsEiqkbRE0npJ90tSLmMkTZXUKmll+pgsaXaX569JuiHd/juSnpP0qKSq7rbN8pjKVdc2Lyvc5sXM+iDrGcv1QGtETAHqgbk5jqkH7oqIpvSxOSKe7nwObADWSWoCqiLiEmA4cFl322Z8TGXrqgvHMWbYYBb7hkkz64Osg2UO8ES6vAy4NMcx9cA1ktZIerDrTEfSEOCsiNgA/Bb4xjG197itnZzBVZXcOGOC27yYWZ9kHSyjgM5/gXYDI3McswW4LSKmAWOBWV3GzwWWAkTESxGxRtKngcPAT3vZ9ghJiyS1SGppa2s7iUMsL9dd/AFOGVTJt1b4hkkzy03WwbITqEuX69LnuYzZBjyZrtsGjOkyfh6wpPOJpKuAPwPmRcTBXrY9IiIWR0RjRDQ2NDT04ZDKW11tNZ+deiY/Wr/DbV7MLCdZB8tSkusekJzyeirHMTcD8yVVAOcBLwCkp7Vmk5wyQ9JpwC3AlRHxTvoa3W5r2bmpaQKB27yYWW6yDpYHgPGSNgBvAVsl3dHLmKXAncACYDXwUERsSsdOBTZFxN70+Q0kp7seTz8BdtNxtrWMuM2LmfWFyvEehcbGxmhpaSl0GSVlY+su5t25kr++4sMsav5gocsxswKQtDYiGnsb5xskLSfnn17HRyeN4p6VbvNiZsfnYLGcLZqVtHlZssFtXsysZw4Wy9nssxv40JihLF7uNi9m1jMHi+VMEgubJ7nNi5kdl4PF+uTqtM3L3Svc5sXMuudgsT7pbPOy4qWdbNqxu9DlmFkRcrBYn1037QMMGVTpWYuZdcvBYn1WN6Saz049w21ezKxbDhY7ITfNmEgA9/1sW6FLMbMi42CxE3LGyCFccf5Y/m31q27zYmZHcbDYCVs0cxLv7jvI99a8WuhSzKyIOFjshLnNi5l1x8FiJ2VRs9u8mNnRHCx2Uma5zYuZHcPBYielouJ3bV5WbnGbFzNzsFgGrr5wHA3DBrN4uW+YNDMHi2VgcFUlN053mxczS2QaLJJqJC2RtF7S/el31vc6RtJUSa3p1w2vlDRZ0uwuz1+TdEMP2/a6T8u/6y9O2rx8y21ezMpe1jOW64HWiJgC1ANzcxxTD9wVEU3pY3NEPN35HNgArOth21z2aXnW2ebl0fU7eH2X27yYlbOsg2UO8ES6vAy4NMcx9cA1ktZIerDrrEPSEOCsiNjQw7a57NP6QWebl3tXbSt0KWZWQFkHyyhgV7q8GxiZ45gtwG0RMQ0YC8zqMn4usPQ42+ayTyQtktQiqaWtra2Ph2W5cJsXM4Psg2UnUJcu16XPcxmzDXgyXbcNGNNl/DxgyXG2zWWfRMTiiGiMiMaGhoacD8j6ZuHMiby77yDfX/NaoUsxswLJOliWApely3OAp3IcczMwX1IFcB7wAkB6Smw2ySmunrbNZZ/WTy44fQSXTBrJPate4cAht3kxK0dZB8sDwHhJG4C3gK2S7uhlzFLgTmABsBp4KCI2pWOnApsiYu9xtu1unRXQouZJvL7LbV7MypXKsQ1HY2NjtLS0FLqMAevw4eDyry+nqrKCH/9ZE/4EuNnAIGltRDT2Ns43SFrmKirEwpmTePH13W7zYlaGHCyWF1df5DYvZuXKwWJ50bXNy4uvu82LWTlxsFjeXHfxmQwZVMndnrWYlRUHi+XNiCGD+INGt3kxKzcOFsur/9Y0kcMR3Oc2L2Zlw8FiedW1zcs7bvNiVhYcLJZ3i5on8c6+g3zPbV7MyoKDxfLObV7MyouDxfqF27yYlQ8Hi/WL2WeP4awxQ1m8/BXKsY2QWTlxsFi/SNq8TOTF13ezasubhS7HzPLIwWL95lMXjWf00MEsXuEbJs0GMgeL9ZvBVZUsmDGB5b9qc5sXswHMwWL96kibF89azAYsB4v1qyNtXn7uNi9mA5WDxfqd27yYDWyZB4ukGklLJK2XdL+6+frA7sZImiqpVdLK9DE5HfuXkp6T9BNJgyTN7zLmDUmzJH1G0pYu6+uyPi7Lzhkjh/AJt3kxG7DyMWO5HmiNiClAPTA3xzH1wF0R0ZQ+NkuaBJwbEZcAPwFOj4jvdY4BXgc2pNv+TZdtd+XhuCxDn0/bvHz/ebd5MRto8hEsc4An0uVlwKU5jqkHrpG0RtKD6UznY0C9pOXATOCVzhdIQ6c9It5Ot/2ipHWSvpGHY7KMXXD6CC6eOJJ7VrrNi9lAk49gGQV0zhh2AyNzHLMFuC0ipgFjgVlAA9AWEc3A6UBTl9eYBzyWLq8F/gJoBD4tacKxO5S0SFKLpJa2trYTPjjLzqLmSezYtZfHNrxe6FLMLEP5CJadQOc1jrr0eS5jtgFPpuu2AWNIQmdzuu5lYHyX15gHLEmXNwLPRcQhoDXd9igRsTgiGiOisaGhoc8HZdm7dHJnm5eX3ebFbADJR7AsBS5Ll+cAT+U45mZgvqQK4DzgBZKZSGM67iyScEHScJLrLZvSn30NaJJUC5wJvJTlAVl+dLZ52eQ2L2YDSj6C5QFgvKQNwFvAVkl39DJmKXAnsABYDTwUEZsi4lngTUnPA5sjYk26/ceBn3Z5vS8DtwMrgb9Lr7tYCbj6Qrd5MRtoVI6nIBobG6OlpaXQZVjqv//rWn78wm8QMG5ELbdcPplPXTS+1+3MrH9JWhsRjb2N8w2SVlAPr9vOss1vABDA9vYObv3hRh5et72whZnZCasqdAFW3r76+Gb2Hjj648YdBw7xl/++gSUbXqd+SDUjhlQzYsggRgyppn7IIEbUHv28prqCbu7DNbMCcbBYQe1o775f2P5Dh2l9ew+/2HGAt/fsf1/4dDWoqoIRtUnI1A2pTsKodhAjTkn+7C6c6mqrqamuzNdhmZU1B4sV1LgRtWzvJlzGj6jlP77UfOT53gOHaN9zgPaO/bz93gF2dezn7T0HknV79tO+Jwmg9o4DvLLzPdr3tNO+5wD7j3PzZW115e8Cp7aa+lOqqesaRLWDjvy8fkg1dem6QVU+g2x2PA4WK6hbLp/MrT/cSMeBQ0fW1VZXcsvlk48aV1NdyWl1lZxWV5Pza0cEHWkgvb1nP7v2HEjCqGP/kUDqGk6/+u27R0Lq4OGeP9RyyqDKo2c/XWdJXYKoa2jV1VZTVelAsvLgYLGC6vz011cf38yO9o5MPxUmiSGDqhgyqIpxI2pz3i4ieHffwTRw0lnSngPsOjIzOjqcdrR30N6RLB8njxhWU3XUqbj6IYPeN2M6NpyG1VRTWXHi148eXrc9L++tlZ7+/F3wx43NMnL4cPDOvoNHnZrb1XGAt9/bnwZPl1lSRxJUb+85wO69B+jpr6EEdbXV7/vAwtHB1CWc0hnU8JoqHvn5jm5ng//4e+c7XMrMw+u2Z/K7kOvHjR0sZgV26HCwuyMJm9+dsvvdjChZ/7trSe0d+2l/7wDv7DvY42tWVoiI6HYGNbiqgukfHJXHI7Ji87Otb7Lv4PuvN44fUcuq/zkn59fJNVh8KsyswCorRP0pg6g/ZRATOSXn7Q4cOsyudCa0K/1QQ+cpufY9B7jzqS3dbrfv4GHefG9/VuVbCeguVKDnT2WeLAeLWYmqrqxg9NDBjB46uNufP7Rue4+fuHv0i03dbGED1Yzbl3X7u9CXa4994Y+pmA1Qt1w+mdpj7tXp7hN3NvD19++CZyxmA1Q+P3FnpaW/fxd88d7MzHLiJpRmZlYQDhYzM8uUg8XMzDLlYDEzs0w5WMzMLFNl+akwSW3Ar09w89HAzgzLybdSqreUaoXSqreUaoXSqreUaoWTq/cDEdHQ26CyDJaTIakll4/bFYtSqreUaoXSqreUaoXSqreUaoX+qdenwszMLFMOFjMzy5SDpe8WF7qAPiqlekupViitekupViitekupVuiHen2NxczMMuUZi5mZZcrBchySqiX9KF2ukbRE0npJ9yvxvnUFrPU7kp6T9KikocVaq6QqST+QtErSPcX+vnap+2ZJT0oaLWmFpI2Sbk9/9r51BapxqqRWSSvTx5Rif28l/WX6e/sTSWOK+L2d3eV9fU3SDcX83ko6RdIj6d+zr/T3762DpQeSaoG1wNx01fVAa0RMAerT9d2tK0StTUBVRFwCDAduKtZagU8B6yNiBjAW+GIR1wqApA8AN6RPvwQ8BkwBPiHp7B7WFUI9cFdENEVEEzCVIn5vJU0Czk1/b38CfJ0ifW8j4uku7+sGoI4ifm+B64Dn0r9n5wL/TD++tw6WHkRER0RcALSmq+YAT6TLy4BLe1hXCL8FvpEuVwB/S/HW+h/A1yRVASOA/9JNXcVSa6dvALemy3OAJyLiMPAMXeo9Zl0h1APXSFoj6UHgYxT3e/sxoF7ScmAmMJHifW8BkDQEOAu4hOJ+b9uBoZIqgVpgOv343jpYcjcK2JUu7wZG9rCu30XESxGxRtKngcPAum7qKpZa342IPcAqkkAs2vcVQNK1wHpgU7qqmOvdAtwWEdNIZoO/101dxVIrQAPQFhHNwOnANIq7XkhmIUsp7t8DgIeAjwNbgRfTevqtXgdL7naSTH9J/9zZw7qCkHQV8GfAPOA33dRVFLVKGiVpMMn/QdUD53VTV1HUmrqS5P+svwf8V5J2GMVa7zbgyS7LhyneWiH5x2xzuvwySc3FXC8kf7+WUOT/HpDMsO+KiAkkgXE2/VivgyV3S4HL0uU5wFM9rOt3kk4DbgGujIh3eqirKGoF/hz4TEQcAvYA/9BNXcVSKxFxbXpefT7JNbdvApdJqgBm0aXeY9YVws3A/LSO80je66J9b0nez87WImeRhEyxvrekF+Nnk5zmKua/YwDDgL3p8j7gWfrxvXWw5O4BYLykDcBbJP9RultXCDeQnPp4XNJKoLqIa/0mcJOkZ4GpszC9AAACiklEQVQ3gW8Xca3d+SfgCpILuI9FxJYe1hXCncACYDXJqZCifm8j4lngTUnPk4TKH1G87y0kH4bYFBF7Ke5/DyD5e/aF9O9ZLfBp+vG99Q2SZmaWKc9YzMwsUw4WMzPLlIPFzMwy5WAxM7NMOVjMzCxTDhazApB0oaQLu1n/f0/w9WZLmnCydZllwcFiVhgXpo+jRMSfnuDrzQYmnEQ9ZpnxfSxmOZB0I3A+yZ3iY4Dfj4hfHDNGJN/Odw5JW53Pktys+gOSvkyvp+v+geSGNYAdETG7y2s83flc0tPpNg1AJfAc8PfAv5M08PxlRCyQ9C8kwbIL+EVEzJc0EviXdL+rI+JL6YzmH4AOoCIibsrivTE7lmcsZrn7KEkTwtuBq7v5+dVAddoC5lXgkyQtyyMipgPfAYZGxF8BXwa+3DVUenArcCZJj6pLgPHAXSSdaCdJOjUi/gi4B/jTiJifbvfXwPcj4qMkHYQvT9fPA77lULF8crCY5e7fImI/8GtgUDc/nwx8NJ1pNAOnAv8JbFTyhXEfA97ryw4jYhvJrOZdQCT9n/4QuJ9k1lLbw6YfIekPRfrnR9Lln0bEc32pwayvHCxmuXu3l59vBr6XzkL+HPglyXWU5yJiHkln5OZ0bAdwChw5hZarhcDDwLUcHVLHvt4vSGY4pH92nrbr7RjMTpqDxSw7jwLj0kag/xt4JX38qaQ1wDigJR37BMmXcj0LNPVhH08A/4ukwWGkrwnwIHCrpNXAJOAfgc+lr98eET89qSMz6wNfvDczs0xVFboAs1Ik6QckX1XQ1VUR8VYh6jErJp6xmJlZpnyNxczMMuVgMTOzTDlYzMwsUw4WMzPLlIPFzMwy9f8BLS5GzutsqJsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(n_estimators,scores,'o-')\n",
    "plt.ylabel(\"mae\")\n",
    "plt.xlabel(\"n_estimator\")\n",
    "print(\"best n_estimator {}\".format(n_estimators[np.argmin(scores)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's l1: 0.245951\tvalid_0's l2: 0.0799026\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[2]\tvalid_0's l1: 0.226678\tvalid_0's l2: 0.0682131\n",
      "[3]\tvalid_0's l1: 0.209595\tvalid_0's l2: 0.0586855\n",
      "[4]\tvalid_0's l1: 0.194338\tvalid_0's l2: 0.0508607\n",
      "[5]\tvalid_0's l1: 0.181069\tvalid_0's l2: 0.0445219\n",
      "[6]\tvalid_0's l1: 0.169189\tvalid_0's l2: 0.0392848\n",
      "[7]\tvalid_0's l1: 0.158873\tvalid_0's l2: 0.0350431\n",
      "[8]\tvalid_0's l1: 0.149769\tvalid_0's l2: 0.031557\n",
      "[9]\tvalid_0's l1: 0.141415\tvalid_0's l2: 0.0285897\n",
      "[10]\tvalid_0's l1: 0.134067\tvalid_0's l2: 0.0261583\n",
      "[11]\tvalid_0's l1: 0.127481\tvalid_0's l2: 0.0240992\n",
      "[12]\tvalid_0's l1: 0.121933\tvalid_0's l2: 0.0224835\n",
      "[13]\tvalid_0's l1: 0.116908\tvalid_0's l2: 0.0210707\n",
      "[14]\tvalid_0's l1: 0.112637\tvalid_0's l2: 0.0199038\n",
      "[15]\tvalid_0's l1: 0.108706\tvalid_0's l2: 0.0188823\n",
      "[16]\tvalid_0's l1: 0.105431\tvalid_0's l2: 0.0180432\n",
      "[17]\tvalid_0's l1: 0.102307\tvalid_0's l2: 0.0172914\n",
      "[18]\tvalid_0's l1: 0.0996523\tvalid_0's l2: 0.0166384\n",
      "[19]\tvalid_0's l1: 0.0972029\tvalid_0's l2: 0.0160783\n",
      "[20]\tvalid_0's l1: 0.0950796\tvalid_0's l2: 0.0156012\n",
      "[21]\tvalid_0's l1: 0.0928554\tvalid_0's l2: 0.0150882\n",
      "[22]\tvalid_0's l1: 0.0912364\tvalid_0's l2: 0.0147276\n",
      "[23]\tvalid_0's l1: 0.0893773\tvalid_0's l2: 0.0143482\n",
      "[24]\tvalid_0's l1: 0.087748\tvalid_0's l2: 0.0139913\n",
      "[25]\tvalid_0's l1: 0.0867009\tvalid_0's l2: 0.0137646\n",
      "[26]\tvalid_0's l1: 0.0855458\tvalid_0's l2: 0.0135216\n",
      "[27]\tvalid_0's l1: 0.0845268\tvalid_0's l2: 0.0132951\n",
      "[28]\tvalid_0's l1: 0.0834586\tvalid_0's l2: 0.01308\n",
      "[29]\tvalid_0's l1: 0.0820494\tvalid_0's l2: 0.0126823\n",
      "[30]\tvalid_0's l1: 0.0810451\tvalid_0's l2: 0.0124618\n",
      "[31]\tvalid_0's l1: 0.0802479\tvalid_0's l2: 0.0122846\n",
      "[32]\tvalid_0's l1: 0.0796107\tvalid_0's l2: 0.0121644\n",
      "[33]\tvalid_0's l1: 0.0790837\tvalid_0's l2: 0.0120704\n",
      "[34]\tvalid_0's l1: 0.0784538\tvalid_0's l2: 0.0119457\n",
      "[35]\tvalid_0's l1: 0.0780126\tvalid_0's l2: 0.0118636\n",
      "[36]\tvalid_0's l1: 0.077716\tvalid_0's l2: 0.0117917\n",
      "[37]\tvalid_0's l1: 0.077197\tvalid_0's l2: 0.0116888\n",
      "[38]\tvalid_0's l1: 0.0768649\tvalid_0's l2: 0.0116143\n",
      "[39]\tvalid_0's l1: 0.0760565\tvalid_0's l2: 0.0114137\n",
      "[40]\tvalid_0's l1: 0.0752823\tvalid_0's l2: 0.0112145\n",
      "[41]\tvalid_0's l1: 0.0749631\tvalid_0's l2: 0.0111483\n",
      "[42]\tvalid_0's l1: 0.0746633\tvalid_0's l2: 0.0110827\n",
      "[43]\tvalid_0's l1: 0.0744063\tvalid_0's l2: 0.011016\n",
      "[44]\tvalid_0's l1: 0.074134\tvalid_0's l2: 0.0109585\n",
      "[45]\tvalid_0's l1: 0.0738961\tvalid_0's l2: 0.010913\n",
      "[46]\tvalid_0's l1: 0.0737403\tvalid_0's l2: 0.0108762\n",
      "[47]\tvalid_0's l1: 0.0731932\tvalid_0's l2: 0.0107357\n",
      "[48]\tvalid_0's l1: 0.0729765\tvalid_0's l2: 0.0106948\n",
      "[49]\tvalid_0's l1: 0.0728205\tvalid_0's l2: 0.0106548\n",
      "[50]\tvalid_0's l1: 0.0722648\tvalid_0's l2: 0.0105136\n",
      "[51]\tvalid_0's l1: 0.0721261\tvalid_0's l2: 0.0104765\n",
      "[52]\tvalid_0's l1: 0.071956\tvalid_0's l2: 0.0104392\n",
      "[53]\tvalid_0's l1: 0.0717767\tvalid_0's l2: 0.0104094\n",
      "[54]\tvalid_0's l1: 0.0716089\tvalid_0's l2: 0.0103866\n",
      "[55]\tvalid_0's l1: 0.071301\tvalid_0's l2: 0.0103142\n",
      "[56]\tvalid_0's l1: 0.0711698\tvalid_0's l2: 0.0102875\n",
      "[57]\tvalid_0's l1: 0.0710264\tvalid_0's l2: 0.0102616\n",
      "[58]\tvalid_0's l1: 0.0708755\tvalid_0's l2: 0.0102362\n",
      "[59]\tvalid_0's l1: 0.0707723\tvalid_0's l2: 0.0102076\n",
      "[60]\tvalid_0's l1: 0.0706688\tvalid_0's l2: 0.0101851\n",
      "[61]\tvalid_0's l1: 0.0705466\tvalid_0's l2: 0.0101611\n",
      "[62]\tvalid_0's l1: 0.070417\tvalid_0's l2: 0.0101418\n",
      "[63]\tvalid_0's l1: 0.0703099\tvalid_0's l2: 0.0101184\n",
      "[64]\tvalid_0's l1: 0.070014\tvalid_0's l2: 0.0100408\n",
      "[65]\tvalid_0's l1: 0.0698032\tvalid_0's l2: 0.00998559\n",
      "[66]\tvalid_0's l1: 0.0695586\tvalid_0's l2: 0.00992395\n",
      "[67]\tvalid_0's l1: 0.0694659\tvalid_0's l2: 0.00991016\n",
      "[68]\tvalid_0's l1: 0.0693805\tvalid_0's l2: 0.00988416\n",
      "[69]\tvalid_0's l1: 0.0692109\tvalid_0's l2: 0.00983863\n",
      "[70]\tvalid_0's l1: 0.0691033\tvalid_0's l2: 0.00981832\n",
      "[71]\tvalid_0's l1: 0.0688559\tvalid_0's l2: 0.00974502\n",
      "[72]\tvalid_0's l1: 0.0687006\tvalid_0's l2: 0.00970238\n",
      "[73]\tvalid_0's l1: 0.06848\tvalid_0's l2: 0.0096301\n",
      "[74]\tvalid_0's l1: 0.0684223\tvalid_0's l2: 0.00961455\n",
      "[75]\tvalid_0's l1: 0.0683508\tvalid_0's l2: 0.00958615\n",
      "[76]\tvalid_0's l1: 0.0683052\tvalid_0's l2: 0.009572\n",
      "[77]\tvalid_0's l1: 0.0681153\tvalid_0's l2: 0.00952969\n",
      "[78]\tvalid_0's l1: 0.0680296\tvalid_0's l2: 0.00951449\n",
      "[79]\tvalid_0's l1: 0.0678274\tvalid_0's l2: 0.00944451\n",
      "[80]\tvalid_0's l1: 0.0677938\tvalid_0's l2: 0.00943237\n",
      "[81]\tvalid_0's l1: 0.0676954\tvalid_0's l2: 0.00940965\n",
      "[82]\tvalid_0's l1: 0.0676028\tvalid_0's l2: 0.0093877\n",
      "[83]\tvalid_0's l1: 0.0674349\tvalid_0's l2: 0.0093459\n",
      "[84]\tvalid_0's l1: 0.0673912\tvalid_0's l2: 0.00932776\n",
      "[85]\tvalid_0's l1: 0.0673384\tvalid_0's l2: 0.00931617\n",
      "[86]\tvalid_0's l1: 0.0672796\tvalid_0's l2: 0.00930222\n",
      "[87]\tvalid_0's l1: 0.0672013\tvalid_0's l2: 0.00928555\n",
      "[88]\tvalid_0's l1: 0.067058\tvalid_0's l2: 0.00923902\n",
      "[89]\tvalid_0's l1: 0.0669635\tvalid_0's l2: 0.0092177\n",
      "[90]\tvalid_0's l1: 0.0668587\tvalid_0's l2: 0.0091912\n",
      "[91]\tvalid_0's l1: 0.0667102\tvalid_0's l2: 0.00913804\n",
      "[92]\tvalid_0's l1: 0.0666526\tvalid_0's l2: 0.00911653\n",
      "[93]\tvalid_0's l1: 0.0666176\tvalid_0's l2: 0.0091075\n",
      "[94]\tvalid_0's l1: 0.0665381\tvalid_0's l2: 0.00908938\n",
      "[95]\tvalid_0's l1: 0.0664094\tvalid_0's l2: 0.00905902\n",
      "[96]\tvalid_0's l1: 0.06634\tvalid_0's l2: 0.00903684\n",
      "[97]\tvalid_0's l1: 0.0662206\tvalid_0's l2: 0.00899158\n",
      "[98]\tvalid_0's l1: 0.0661688\tvalid_0's l2: 0.0089855\n",
      "[99]\tvalid_0's l1: 0.0660773\tvalid_0's l2: 0.00896682\n",
      "[100]\tvalid_0's l1: 0.0659306\tvalid_0's l2: 0.00892838\n",
      "[101]\tvalid_0's l1: 0.0658745\tvalid_0's l2: 0.0089192\n",
      "[102]\tvalid_0's l1: 0.065791\tvalid_0's l2: 0.00888833\n",
      "[103]\tvalid_0's l1: 0.0656774\tvalid_0's l2: 0.00885243\n",
      "[104]\tvalid_0's l1: 0.0655935\tvalid_0's l2: 0.00882508\n",
      "[105]\tvalid_0's l1: 0.0655746\tvalid_0's l2: 0.00881604\n",
      "[106]\tvalid_0's l1: 0.0655173\tvalid_0's l2: 0.00880362\n",
      "[107]\tvalid_0's l1: 0.0653877\tvalid_0's l2: 0.00877138\n",
      "[108]\tvalid_0's l1: 0.065285\tvalid_0's l2: 0.00874944\n",
      "[109]\tvalid_0's l1: 0.0652656\tvalid_0's l2: 0.00874508\n",
      "[110]\tvalid_0's l1: 0.0652144\tvalid_0's l2: 0.00873215\n",
      "[111]\tvalid_0's l1: 0.0650925\tvalid_0's l2: 0.00870511\n",
      "[112]\tvalid_0's l1: 0.0650302\tvalid_0's l2: 0.00867355\n",
      "[113]\tvalid_0's l1: 0.064989\tvalid_0's l2: 0.00866171\n",
      "[114]\tvalid_0's l1: 0.0649491\tvalid_0's l2: 0.00864751\n",
      "[115]\tvalid_0's l1: 0.0649154\tvalid_0's l2: 0.00864013\n",
      "[116]\tvalid_0's l1: 0.0647886\tvalid_0's l2: 0.00860299\n",
      "[117]\tvalid_0's l1: 0.0647248\tvalid_0's l2: 0.00859226\n",
      "[118]\tvalid_0's l1: 0.0646513\tvalid_0's l2: 0.00856838\n",
      "[119]\tvalid_0's l1: 0.0645208\tvalid_0's l2: 0.00853253\n",
      "[120]\tvalid_0's l1: 0.0644689\tvalid_0's l2: 0.00852473\n",
      "[121]\tvalid_0's l1: 0.0643448\tvalid_0's l2: 0.00849375\n",
      "[122]\tvalid_0's l1: 0.064289\tvalid_0's l2: 0.00847424\n",
      "[123]\tvalid_0's l1: 0.0642224\tvalid_0's l2: 0.00844912\n",
      "[124]\tvalid_0's l1: 0.0642043\tvalid_0's l2: 0.0084414\n",
      "[125]\tvalid_0's l1: 0.0641053\tvalid_0's l2: 0.00841524\n",
      "[126]\tvalid_0's l1: 0.0640266\tvalid_0's l2: 0.00838405\n",
      "[127]\tvalid_0's l1: 0.0639821\tvalid_0's l2: 0.00837852\n",
      "[128]\tvalid_0's l1: 0.0639547\tvalid_0's l2: 0.0083617\n",
      "[129]\tvalid_0's l1: 0.0639358\tvalid_0's l2: 0.00835842\n",
      "[130]\tvalid_0's l1: 0.0638841\tvalid_0's l2: 0.00834969\n",
      "[131]\tvalid_0's l1: 0.0638538\tvalid_0's l2: 0.0083397\n",
      "[132]\tvalid_0's l1: 0.063819\tvalid_0's l2: 0.00833278\n",
      "[133]\tvalid_0's l1: 0.0637716\tvalid_0's l2: 0.00832131\n",
      "[134]\tvalid_0's l1: 0.0637488\tvalid_0's l2: 0.0083152\n",
      "[135]\tvalid_0's l1: 0.0637281\tvalid_0's l2: 0.0083078\n",
      "[136]\tvalid_0's l1: 0.0636638\tvalid_0's l2: 0.00828725\n",
      "[137]\tvalid_0's l1: 0.0636322\tvalid_0's l2: 0.00828142\n",
      "[138]\tvalid_0's l1: 0.0635598\tvalid_0's l2: 0.00826705\n",
      "[139]\tvalid_0's l1: 0.0635517\tvalid_0's l2: 0.00826326\n",
      "[140]\tvalid_0's l1: 0.0635109\tvalid_0's l2: 0.00825556\n",
      "[141]\tvalid_0's l1: 0.0634626\tvalid_0's l2: 0.0082434\n",
      "[142]\tvalid_0's l1: 0.063427\tvalid_0's l2: 0.0082356\n",
      "[143]\tvalid_0's l1: 0.0633992\tvalid_0's l2: 0.00823178\n",
      "[144]\tvalid_0's l1: 0.0633714\tvalid_0's l2: 0.00822117\n",
      "[145]\tvalid_0's l1: 0.0632689\tvalid_0's l2: 0.00819275\n",
      "[146]\tvalid_0's l1: 0.0632355\tvalid_0's l2: 0.0081842\n",
      "[147]\tvalid_0's l1: 0.0631734\tvalid_0's l2: 0.00816914\n",
      "[148]\tvalid_0's l1: 0.0631554\tvalid_0's l2: 0.00816342\n",
      "[149]\tvalid_0's l1: 0.0630915\tvalid_0's l2: 0.00814223\n",
      "[150]\tvalid_0's l1: 0.0630257\tvalid_0's l2: 0.00812864\n",
      "[151]\tvalid_0's l1: 0.0629674\tvalid_0's l2: 0.00810772\n",
      "[152]\tvalid_0's l1: 0.0628928\tvalid_0's l2: 0.00809157\n",
      "[153]\tvalid_0's l1: 0.062862\tvalid_0's l2: 0.00808326\n",
      "[154]\tvalid_0's l1: 0.0628367\tvalid_0's l2: 0.00807544\n",
      "[155]\tvalid_0's l1: 0.0628224\tvalid_0's l2: 0.00807054\n",
      "[156]\tvalid_0's l1: 0.0627607\tvalid_0's l2: 0.00805709\n",
      "[157]\tvalid_0's l1: 0.0627235\tvalid_0's l2: 0.00804171\n",
      "[158]\tvalid_0's l1: 0.0626681\tvalid_0's l2: 0.00802948\n",
      "[159]\tvalid_0's l1: 0.0626548\tvalid_0's l2: 0.00802659\n",
      "[160]\tvalid_0's l1: 0.0625744\tvalid_0's l2: 0.00800215\n",
      "[161]\tvalid_0's l1: 0.0625507\tvalid_0's l2: 0.00799773\n",
      "[162]\tvalid_0's l1: 0.0625062\tvalid_0's l2: 0.00798584\n",
      "[163]\tvalid_0's l1: 0.0624752\tvalid_0's l2: 0.00797277\n",
      "[164]\tvalid_0's l1: 0.0624702\tvalid_0's l2: 0.0079688\n",
      "[165]\tvalid_0's l1: 0.0624439\tvalid_0's l2: 0.00796345\n",
      "[166]\tvalid_0's l1: 0.0624375\tvalid_0's l2: 0.00796167\n",
      "[167]\tvalid_0's l1: 0.0624021\tvalid_0's l2: 0.00795622\n",
      "[168]\tvalid_0's l1: 0.0623861\tvalid_0's l2: 0.0079532\n",
      "[169]\tvalid_0's l1: 0.0623754\tvalid_0's l2: 0.00795102\n",
      "[170]\tvalid_0's l1: 0.0623463\tvalid_0's l2: 0.00794603\n",
      "[171]\tvalid_0's l1: 0.0622781\tvalid_0's l2: 0.00793724\n",
      "[172]\tvalid_0's l1: 0.0622417\tvalid_0's l2: 0.00792782\n",
      "[173]\tvalid_0's l1: 0.0622139\tvalid_0's l2: 0.00791893\n",
      "[174]\tvalid_0's l1: 0.0621415\tvalid_0's l2: 0.00790142\n",
      "[175]\tvalid_0's l1: 0.0621292\tvalid_0's l2: 0.00790014\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[176]\tvalid_0's l1: 0.0621091\tvalid_0's l2: 0.00789475\n",
      "[177]\tvalid_0's l1: 0.0620937\tvalid_0's l2: 0.00789072\n",
      "[178]\tvalid_0's l1: 0.0620923\tvalid_0's l2: 0.00788742\n",
      "[179]\tvalid_0's l1: 0.0620695\tvalid_0's l2: 0.00788345\n",
      "[180]\tvalid_0's l1: 0.0620561\tvalid_0's l2: 0.00787245\n",
      "[181]\tvalid_0's l1: 0.0619938\tvalid_0's l2: 0.00785701\n",
      "[182]\tvalid_0's l1: 0.0619728\tvalid_0's l2: 0.00785054\n",
      "[183]\tvalid_0's l1: 0.0619483\tvalid_0's l2: 0.00784426\n",
      "[184]\tvalid_0's l1: 0.0618916\tvalid_0's l2: 0.00782893\n",
      "[185]\tvalid_0's l1: 0.0618882\tvalid_0's l2: 0.00782821\n",
      "[186]\tvalid_0's l1: 0.0617958\tvalid_0's l2: 0.00780013\n",
      "[187]\tvalid_0's l1: 0.0617874\tvalid_0's l2: 0.00779428\n",
      "[188]\tvalid_0's l1: 0.0617613\tvalid_0's l2: 0.00778537\n",
      "[189]\tvalid_0's l1: 0.0617498\tvalid_0's l2: 0.00778327\n",
      "[190]\tvalid_0's l1: 0.0617415\tvalid_0's l2: 0.00778013\n",
      "[191]\tvalid_0's l1: 0.0617271\tvalid_0's l2: 0.00777387\n",
      "[192]\tvalid_0's l1: 0.0617194\tvalid_0's l2: 0.00777251\n",
      "[193]\tvalid_0's l1: 0.0617034\tvalid_0's l2: 0.00776778\n",
      "[194]\tvalid_0's l1: 0.0616932\tvalid_0's l2: 0.00776213\n",
      "[195]\tvalid_0's l1: 0.0616458\tvalid_0's l2: 0.00775323\n",
      "[196]\tvalid_0's l1: 0.0616342\tvalid_0's l2: 0.0077498\n",
      "[197]\tvalid_0's l1: 0.0616099\tvalid_0's l2: 0.00774467\n",
      "[198]\tvalid_0's l1: 0.0615941\tvalid_0's l2: 0.00774147\n",
      "[199]\tvalid_0's l1: 0.0615867\tvalid_0's l2: 0.00773916\n",
      "[200]\tvalid_0's l1: 0.0615496\tvalid_0's l2: 0.00773484\n",
      "[201]\tvalid_0's l1: 0.0615442\tvalid_0's l2: 0.00773016\n",
      "[202]\tvalid_0's l1: 0.0615308\tvalid_0's l2: 0.00772869\n",
      "[203]\tvalid_0's l1: 0.061527\tvalid_0's l2: 0.00772763\n",
      "[204]\tvalid_0's l1: 0.0615127\tvalid_0's l2: 0.00772413\n",
      "[205]\tvalid_0's l1: 0.0614676\tvalid_0's l2: 0.00770868\n",
      "[206]\tvalid_0's l1: 0.0614456\tvalid_0's l2: 0.00770356\n",
      "[207]\tvalid_0's l1: 0.0614349\tvalid_0's l2: 0.00770067\n",
      "[208]\tvalid_0's l1: 0.0614148\tvalid_0's l2: 0.00769728\n",
      "[209]\tvalid_0's l1: 0.061393\tvalid_0's l2: 0.00769306\n",
      "[210]\tvalid_0's l1: 0.0613878\tvalid_0's l2: 0.00769176\n",
      "[211]\tvalid_0's l1: 0.0613715\tvalid_0's l2: 0.00768739\n",
      "[212]\tvalid_0's l1: 0.0613692\tvalid_0's l2: 0.00768421\n",
      "[213]\tvalid_0's l1: 0.0613009\tvalid_0's l2: 0.00767403\n",
      "[214]\tvalid_0's l1: 0.061259\tvalid_0's l2: 0.00766591\n",
      "[215]\tvalid_0's l1: 0.0612563\tvalid_0's l2: 0.00766338\n",
      "[216]\tvalid_0's l1: 0.0612416\tvalid_0's l2: 0.00766072\n",
      "[217]\tvalid_0's l1: 0.0612324\tvalid_0's l2: 0.00765844\n",
      "[218]\tvalid_0's l1: 0.0612154\tvalid_0's l2: 0.00765249\n",
      "[219]\tvalid_0's l1: 0.0611909\tvalid_0's l2: 0.00764667\n",
      "[220]\tvalid_0's l1: 0.0611841\tvalid_0's l2: 0.00764471\n",
      "[221]\tvalid_0's l1: 0.0611569\tvalid_0's l2: 0.00763774\n",
      "[222]\tvalid_0's l1: 0.0611428\tvalid_0's l2: 0.00763603\n",
      "[223]\tvalid_0's l1: 0.0611038\tvalid_0's l2: 0.00762949\n",
      "[224]\tvalid_0's l1: 0.0610883\tvalid_0's l2: 0.00762472\n",
      "[225]\tvalid_0's l1: 0.0610858\tvalid_0's l2: 0.00762472\n",
      "[226]\tvalid_0's l1: 0.0610785\tvalid_0's l2: 0.00762216\n",
      "[227]\tvalid_0's l1: 0.0610739\tvalid_0's l2: 0.00762143\n",
      "[228]\tvalid_0's l1: 0.0610678\tvalid_0's l2: 0.00761936\n",
      "[229]\tvalid_0's l1: 0.0610439\tvalid_0's l2: 0.00761486\n",
      "[230]\tvalid_0's l1: 0.0610175\tvalid_0's l2: 0.00760908\n",
      "[231]\tvalid_0's l1: 0.0609923\tvalid_0's l2: 0.00760436\n",
      "[232]\tvalid_0's l1: 0.0609525\tvalid_0's l2: 0.00759502\n",
      "[233]\tvalid_0's l1: 0.0609434\tvalid_0's l2: 0.00759245\n",
      "[234]\tvalid_0's l1: 0.0609346\tvalid_0's l2: 0.00758803\n",
      "[235]\tvalid_0's l1: 0.0609314\tvalid_0's l2: 0.00758588\n",
      "[236]\tvalid_0's l1: 0.0609282\tvalid_0's l2: 0.0075855\n",
      "[237]\tvalid_0's l1: 0.0609238\tvalid_0's l2: 0.00758473\n",
      "[238]\tvalid_0's l1: 0.060858\tvalid_0's l2: 0.0075607\n",
      "[239]\tvalid_0's l1: 0.0608407\tvalid_0's l2: 0.00755626\n",
      "[240]\tvalid_0's l1: 0.0608279\tvalid_0's l2: 0.00755207\n",
      "[241]\tvalid_0's l1: 0.060826\tvalid_0's l2: 0.0075503\n",
      "[242]\tvalid_0's l1: 0.0607948\tvalid_0's l2: 0.00754333\n",
      "[243]\tvalid_0's l1: 0.0607598\tvalid_0's l2: 0.00753284\n",
      "[244]\tvalid_0's l1: 0.0607413\tvalid_0's l2: 0.00753085\n",
      "[245]\tvalid_0's l1: 0.0607301\tvalid_0's l2: 0.00752813\n",
      "[246]\tvalid_0's l1: 0.0607071\tvalid_0's l2: 0.00752255\n",
      "[247]\tvalid_0's l1: 0.0607026\tvalid_0's l2: 0.00752137\n",
      "[248]\tvalid_0's l1: 0.0606923\tvalid_0's l2: 0.00751851\n",
      "[249]\tvalid_0's l1: 0.0606846\tvalid_0's l2: 0.00751692\n",
      "[250]\tvalid_0's l1: 0.0606751\tvalid_0's l2: 0.00751755\n",
      "[251]\tvalid_0's l1: 0.0606774\tvalid_0's l2: 0.00751856\n",
      "[252]\tvalid_0's l1: 0.0606561\tvalid_0's l2: 0.00751333\n",
      "[253]\tvalid_0's l1: 0.0606549\tvalid_0's l2: 0.00751255\n",
      "[254]\tvalid_0's l1: 0.0606527\tvalid_0's l2: 0.0075117\n",
      "[255]\tvalid_0's l1: 0.0606451\tvalid_0's l2: 0.00750979\n",
      "[256]\tvalid_0's l1: 0.0606465\tvalid_0's l2: 0.00750681\n",
      "[257]\tvalid_0's l1: 0.0606421\tvalid_0's l2: 0.00750484\n",
      "[258]\tvalid_0's l1: 0.0605991\tvalid_0's l2: 0.00749648\n",
      "[259]\tvalid_0's l1: 0.0605624\tvalid_0's l2: 0.00748836\n",
      "[260]\tvalid_0's l1: 0.0605564\tvalid_0's l2: 0.00748607\n",
      "[261]\tvalid_0's l1: 0.060547\tvalid_0's l2: 0.00747954\n",
      "[262]\tvalid_0's l1: 0.060511\tvalid_0's l2: 0.00747297\n",
      "[263]\tvalid_0's l1: 0.0604954\tvalid_0's l2: 0.00746919\n",
      "[264]\tvalid_0's l1: 0.0604873\tvalid_0's l2: 0.00746591\n",
      "[265]\tvalid_0's l1: 0.0604839\tvalid_0's l2: 0.00746563\n",
      "[266]\tvalid_0's l1: 0.0604628\tvalid_0's l2: 0.00745981\n",
      "[267]\tvalid_0's l1: 0.0604221\tvalid_0's l2: 0.0074482\n",
      "[268]\tvalid_0's l1: 0.0604221\tvalid_0's l2: 0.00744695\n",
      "[269]\tvalid_0's l1: 0.0604059\tvalid_0's l2: 0.00744225\n",
      "[270]\tvalid_0's l1: 0.060387\tvalid_0's l2: 0.00743872\n",
      "[271]\tvalid_0's l1: 0.0603783\tvalid_0's l2: 0.00743707\n",
      "[272]\tvalid_0's l1: 0.0603586\tvalid_0's l2: 0.00743586\n",
      "[273]\tvalid_0's l1: 0.0603492\tvalid_0's l2: 0.00743342\n",
      "[274]\tvalid_0's l1: 0.0603445\tvalid_0's l2: 0.00743217\n",
      "[275]\tvalid_0's l1: 0.0603204\tvalid_0's l2: 0.00742736\n",
      "[276]\tvalid_0's l1: 0.0602933\tvalid_0's l2: 0.0074182\n",
      "[277]\tvalid_0's l1: 0.0602856\tvalid_0's l2: 0.00741636\n",
      "[278]\tvalid_0's l1: 0.0602808\tvalid_0's l2: 0.00741444\n",
      "[279]\tvalid_0's l1: 0.0602755\tvalid_0's l2: 0.00741346\n",
      "[280]\tvalid_0's l1: 0.060249\tvalid_0's l2: 0.0074111\n",
      "[281]\tvalid_0's l1: 0.060246\tvalid_0's l2: 0.00740991\n",
      "[282]\tvalid_0's l1: 0.0602039\tvalid_0's l2: 0.00740377\n",
      "[283]\tvalid_0's l1: 0.0601958\tvalid_0's l2: 0.00739957\n",
      "[284]\tvalid_0's l1: 0.0601844\tvalid_0's l2: 0.00739784\n",
      "[285]\tvalid_0's l1: 0.0601649\tvalid_0's l2: 0.00738912\n",
      "[286]\tvalid_0's l1: 0.0601514\tvalid_0's l2: 0.00738531\n",
      "[287]\tvalid_0's l1: 0.060144\tvalid_0's l2: 0.0073847\n",
      "[288]\tvalid_0's l1: 0.0601185\tvalid_0's l2: 0.00737609\n",
      "[289]\tvalid_0's l1: 0.060093\tvalid_0's l2: 0.00736866\n",
      "[290]\tvalid_0's l1: 0.0600861\tvalid_0's l2: 0.00736682\n",
      "[291]\tvalid_0's l1: 0.060082\tvalid_0's l2: 0.00736437\n",
      "[292]\tvalid_0's l1: 0.0600697\tvalid_0's l2: 0.00736088\n",
      "[293]\tvalid_0's l1: 0.0600644\tvalid_0's l2: 0.00736019\n",
      "[294]\tvalid_0's l1: 0.0600478\tvalid_0's l2: 0.00735573\n",
      "[295]\tvalid_0's l1: 0.0600324\tvalid_0's l2: 0.00735283\n",
      "[296]\tvalid_0's l1: 0.0600261\tvalid_0's l2: 0.00735116\n",
      "[297]\tvalid_0's l1: 0.060015\tvalid_0's l2: 0.00734777\n",
      "[298]\tvalid_0's l1: 0.0599943\tvalid_0's l2: 0.00734292\n",
      "[299]\tvalid_0's l1: 0.0599897\tvalid_0's l2: 0.0073379\n",
      "[300]\tvalid_0's l1: 0.0599743\tvalid_0's l2: 0.00733289\n",
      "[301]\tvalid_0's l1: 0.0599677\tvalid_0's l2: 0.00733165\n",
      "[302]\tvalid_0's l1: 0.0599564\tvalid_0's l2: 0.00732815\n",
      "[303]\tvalid_0's l1: 0.0599186\tvalid_0's l2: 0.00731642\n",
      "[304]\tvalid_0's l1: 0.0599057\tvalid_0's l2: 0.00731443\n",
      "[305]\tvalid_0's l1: 0.0598899\tvalid_0's l2: 0.00731051\n",
      "[306]\tvalid_0's l1: 0.0598758\tvalid_0's l2: 0.00730785\n",
      "[307]\tvalid_0's l1: 0.059867\tvalid_0's l2: 0.00730554\n",
      "[308]\tvalid_0's l1: 0.0598299\tvalid_0's l2: 0.00729924\n",
      "[309]\tvalid_0's l1: 0.0598213\tvalid_0's l2: 0.00729622\n",
      "[310]\tvalid_0's l1: 0.0598019\tvalid_0's l2: 0.00729078\n",
      "[311]\tvalid_0's l1: 0.0597985\tvalid_0's l2: 0.00729032\n",
      "[312]\tvalid_0's l1: 0.0597968\tvalid_0's l2: 0.00728993\n",
      "[313]\tvalid_0's l1: 0.0597889\tvalid_0's l2: 0.00728781\n",
      "[314]\tvalid_0's l1: 0.0597828\tvalid_0's l2: 0.00728647\n",
      "[315]\tvalid_0's l1: 0.0597752\tvalid_0's l2: 0.00728308\n",
      "[316]\tvalid_0's l1: 0.0597313\tvalid_0's l2: 0.00727217\n",
      "[317]\tvalid_0's l1: 0.0597099\tvalid_0's l2: 0.00727033\n",
      "[318]\tvalid_0's l1: 0.0597052\tvalid_0's l2: 0.00726923\n",
      "[319]\tvalid_0's l1: 0.0596815\tvalid_0's l2: 0.00726406\n",
      "[320]\tvalid_0's l1: 0.0596641\tvalid_0's l2: 0.00725938\n",
      "[321]\tvalid_0's l1: 0.0596434\tvalid_0's l2: 0.00725662\n",
      "[322]\tvalid_0's l1: 0.0596271\tvalid_0's l2: 0.00725326\n",
      "[323]\tvalid_0's l1: 0.0596116\tvalid_0's l2: 0.00724937\n",
      "[324]\tvalid_0's l1: 0.0596097\tvalid_0's l2: 0.00724817\n",
      "[325]\tvalid_0's l1: 0.0596057\tvalid_0's l2: 0.00724688\n",
      "[326]\tvalid_0's l1: 0.0596048\tvalid_0's l2: 0.00724418\n",
      "[327]\tvalid_0's l1: 0.0596012\tvalid_0's l2: 0.00724299\n",
      "[328]\tvalid_0's l1: 0.059593\tvalid_0's l2: 0.00724168\n",
      "[329]\tvalid_0's l1: 0.0595802\tvalid_0's l2: 0.00723676\n",
      "[330]\tvalid_0's l1: 0.0595807\tvalid_0's l2: 0.00723609\n",
      "[331]\tvalid_0's l1: 0.0595654\tvalid_0's l2: 0.00723407\n",
      "[332]\tvalid_0's l1: 0.059557\tvalid_0's l2: 0.00723122\n",
      "[333]\tvalid_0's l1: 0.0595544\tvalid_0's l2: 0.00723015\n",
      "[334]\tvalid_0's l1: 0.0595488\tvalid_0's l2: 0.00722858\n",
      "[335]\tvalid_0's l1: 0.0595438\tvalid_0's l2: 0.00722837\n",
      "[336]\tvalid_0's l1: 0.0595339\tvalid_0's l2: 0.00722712\n",
      "[337]\tvalid_0's l1: 0.0595153\tvalid_0's l2: 0.00722262\n",
      "[338]\tvalid_0's l1: 0.0595139\tvalid_0's l2: 0.00722096\n",
      "[339]\tvalid_0's l1: 0.0594884\tvalid_0's l2: 0.0072162\n",
      "[340]\tvalid_0's l1: 0.059482\tvalid_0's l2: 0.00721469\n",
      "[341]\tvalid_0's l1: 0.0594803\tvalid_0's l2: 0.00721375\n",
      "[342]\tvalid_0's l1: 0.0594763\tvalid_0's l2: 0.0072131\n",
      "[343]\tvalid_0's l1: 0.0594707\tvalid_0's l2: 0.00721182\n",
      "[344]\tvalid_0's l1: 0.0594612\tvalid_0's l2: 0.00721005\n",
      "[345]\tvalid_0's l1: 0.0594647\tvalid_0's l2: 0.00721021\n",
      "[346]\tvalid_0's l1: 0.0594596\tvalid_0's l2: 0.00721017\n",
      "[347]\tvalid_0's l1: 0.0594374\tvalid_0's l2: 0.00720848\n",
      "[348]\tvalid_0's l1: 0.0594418\tvalid_0's l2: 0.00720804\n",
      "[349]\tvalid_0's l1: 0.0594385\tvalid_0's l2: 0.00720749\n",
      "[350]\tvalid_0's l1: 0.0594349\tvalid_0's l2: 0.00720642\n",
      "[351]\tvalid_0's l1: 0.0594172\tvalid_0's l2: 0.00720432\n",
      "[352]\tvalid_0's l1: 0.0594158\tvalid_0's l2: 0.00720268\n",
      "[353]\tvalid_0's l1: 0.0594119\tvalid_0's l2: 0.00720244\n",
      "[354]\tvalid_0's l1: 0.0594084\tvalid_0's l2: 0.00720234\n",
      "[355]\tvalid_0's l1: 0.0594068\tvalid_0's l2: 0.00720142\n",
      "[356]\tvalid_0's l1: 0.0593827\tvalid_0's l2: 0.00719698\n",
      "[357]\tvalid_0's l1: 0.0593672\tvalid_0's l2: 0.00719361\n",
      "[358]\tvalid_0's l1: 0.0593657\tvalid_0's l2: 0.0071925\n",
      "[359]\tvalid_0's l1: 0.0593621\tvalid_0's l2: 0.00719183\n",
      "[360]\tvalid_0's l1: 0.0593578\tvalid_0's l2: 0.00719048\n",
      "[361]\tvalid_0's l1: 0.0593317\tvalid_0's l2: 0.00718509\n",
      "[362]\tvalid_0's l1: 0.0593165\tvalid_0's l2: 0.00718096\n",
      "[363]\tvalid_0's l1: 0.0592964\tvalid_0's l2: 0.00717612\n",
      "[364]\tvalid_0's l1: 0.0592904\tvalid_0's l2: 0.00717273\n",
      "[365]\tvalid_0's l1: 0.0592745\tvalid_0's l2: 0.00716989\n",
      "[366]\tvalid_0's l1: 0.0592595\tvalid_0's l2: 0.00716674\n",
      "[367]\tvalid_0's l1: 0.0592458\tvalid_0's l2: 0.00716225\n",
      "[368]\tvalid_0's l1: 0.059247\tvalid_0's l2: 0.00716195\n",
      "[369]\tvalid_0's l1: 0.0592436\tvalid_0's l2: 0.00716071\n",
      "[370]\tvalid_0's l1: 0.0592314\tvalid_0's l2: 0.00715802\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[371]\tvalid_0's l1: 0.0592317\tvalid_0's l2: 0.00715905\n",
      "[372]\tvalid_0's l1: 0.0592257\tvalid_0's l2: 0.00715697\n",
      "[373]\tvalid_0's l1: 0.0592199\tvalid_0's l2: 0.00715637\n",
      "[374]\tvalid_0's l1: 0.0592168\tvalid_0's l2: 0.00715498\n",
      "[375]\tvalid_0's l1: 0.0592069\tvalid_0's l2: 0.0071537\n",
      "[376]\tvalid_0's l1: 0.0591999\tvalid_0's l2: 0.00715176\n",
      "[377]\tvalid_0's l1: 0.0591984\tvalid_0's l2: 0.00714892\n",
      "[378]\tvalid_0's l1: 0.0591692\tvalid_0's l2: 0.00713848\n",
      "[379]\tvalid_0's l1: 0.0591685\tvalid_0's l2: 0.00713698\n",
      "[380]\tvalid_0's l1: 0.0591578\tvalid_0's l2: 0.00713306\n",
      "[381]\tvalid_0's l1: 0.0591421\tvalid_0's l2: 0.00713099\n",
      "[382]\tvalid_0's l1: 0.0591172\tvalid_0's l2: 0.00712579\n",
      "[383]\tvalid_0's l1: 0.0591006\tvalid_0's l2: 0.0071224\n",
      "[384]\tvalid_0's l1: 0.059097\tvalid_0's l2: 0.00712188\n",
      "[385]\tvalid_0's l1: 0.059095\tvalid_0's l2: 0.00712107\n",
      "[386]\tvalid_0's l1: 0.0590862\tvalid_0's l2: 0.00711945\n",
      "[387]\tvalid_0's l1: 0.0590644\tvalid_0's l2: 0.00711505\n",
      "[388]\tvalid_0's l1: 0.0590607\tvalid_0's l2: 0.00711506\n",
      "[389]\tvalid_0's l1: 0.0590575\tvalid_0's l2: 0.00711392\n",
      "[390]\tvalid_0's l1: 0.0590417\tvalid_0's l2: 0.00711146\n",
      "[391]\tvalid_0's l1: 0.0590355\tvalid_0's l2: 0.00711067\n",
      "[392]\tvalid_0's l1: 0.0590286\tvalid_0's l2: 0.00710778\n",
      "[393]\tvalid_0's l1: 0.0590216\tvalid_0's l2: 0.00710659\n",
      "[394]\tvalid_0's l1: 0.0590099\tvalid_0's l2: 0.00710301\n",
      "[395]\tvalid_0's l1: 0.0590069\tvalid_0's l2: 0.00710208\n",
      "[396]\tvalid_0's l1: 0.0590034\tvalid_0's l2: 0.00710047\n",
      "[397]\tvalid_0's l1: 0.0589909\tvalid_0's l2: 0.0070936\n",
      "[398]\tvalid_0's l1: 0.0589901\tvalid_0's l2: 0.0070914\n",
      "[399]\tvalid_0's l1: 0.0589854\tvalid_0's l2: 0.00708941\n",
      "[400]\tvalid_0's l1: 0.0589705\tvalid_0's l2: 0.00708773\n",
      "[401]\tvalid_0's l1: 0.0589625\tvalid_0's l2: 0.00708597\n",
      "[402]\tvalid_0's l1: 0.0589644\tvalid_0's l2: 0.00708488\n",
      "[403]\tvalid_0's l1: 0.0589634\tvalid_0's l2: 0.00708438\n",
      "[404]\tvalid_0's l1: 0.0589565\tvalid_0's l2: 0.00708323\n",
      "[405]\tvalid_0's l1: 0.0589561\tvalid_0's l2: 0.00708292\n",
      "[406]\tvalid_0's l1: 0.0589485\tvalid_0's l2: 0.00708057\n",
      "[407]\tvalid_0's l1: 0.0589426\tvalid_0's l2: 0.00707874\n",
      "[408]\tvalid_0's l1: 0.0589328\tvalid_0's l2: 0.0070761\n",
      "[409]\tvalid_0's l1: 0.058926\tvalid_0's l2: 0.007073\n",
      "[410]\tvalid_0's l1: 0.0589203\tvalid_0's l2: 0.00707211\n",
      "[411]\tvalid_0's l1: 0.0588941\tvalid_0's l2: 0.00706425\n",
      "[412]\tvalid_0's l1: 0.058887\tvalid_0's l2: 0.00706222\n",
      "[413]\tvalid_0's l1: 0.0588788\tvalid_0's l2: 0.00705912\n",
      "[414]\tvalid_0's l1: 0.0588677\tvalid_0's l2: 0.00705691\n",
      "[415]\tvalid_0's l1: 0.0588715\tvalid_0's l2: 0.00706103\n",
      "[416]\tvalid_0's l1: 0.0588646\tvalid_0's l2: 0.00705964\n",
      "[417]\tvalid_0's l1: 0.0588604\tvalid_0's l2: 0.00705951\n",
      "[418]\tvalid_0's l1: 0.0588378\tvalid_0's l2: 0.00705833\n",
      "[419]\tvalid_0's l1: 0.0588364\tvalid_0's l2: 0.00705713\n",
      "Early stopping, best iteration is:\n",
      "[414]\tvalid_0's l1: 0.0588677\tvalid_0's l2: 0.00705691\n",
      "本次结果输出的mae值是:\n",
      " 0.058867698663447106\n",
      "[1]\tvalid_0's l1: 0.244506\tvalid_0's l2: 0.0790859\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[2]\tvalid_0's l1: 0.223857\tvalid_0's l2: 0.0667341\n",
      "[3]\tvalid_0's l1: 0.205626\tvalid_0's l2: 0.0566941\n",
      "[4]\tvalid_0's l1: 0.189298\tvalid_0's l2: 0.0484539\n",
      "[5]\tvalid_0's l1: 0.175104\tvalid_0's l2: 0.0417921\n",
      "[6]\tvalid_0's l1: 0.16227\tvalid_0's l2: 0.036285\n",
      "[7]\tvalid_0's l1: 0.150898\tvalid_0's l2: 0.0317221\n",
      "[8]\tvalid_0's l1: 0.140883\tvalid_0's l2: 0.0280196\n",
      "[9]\tvalid_0's l1: 0.131939\tvalid_0's l2: 0.0249613\n",
      "[10]\tvalid_0's l1: 0.124375\tvalid_0's l2: 0.0224944\n",
      "[11]\tvalid_0's l1: 0.117467\tvalid_0's l2: 0.0204263\n",
      "[12]\tvalid_0's l1: 0.110957\tvalid_0's l2: 0.0185453\n",
      "[13]\tvalid_0's l1: 0.105077\tvalid_0's l2: 0.016929\n",
      "[14]\tvalid_0's l1: 0.100272\tvalid_0's l2: 0.0157152\n",
      "[15]\tvalid_0's l1: 0.0960833\tvalid_0's l2: 0.0147431\n",
      "[16]\tvalid_0's l1: 0.0920567\tvalid_0's l2: 0.0137979\n",
      "[17]\tvalid_0's l1: 0.088268\tvalid_0's l2: 0.0129121\n",
      "[18]\tvalid_0's l1: 0.0854499\tvalid_0's l2: 0.0123437\n",
      "[19]\tvalid_0's l1: 0.0827065\tvalid_0's l2: 0.0117755\n",
      "[20]\tvalid_0's l1: 0.0800967\tvalid_0's l2: 0.0112162\n",
      "[21]\tvalid_0's l1: 0.0781237\tvalid_0's l2: 0.0108399\n",
      "[22]\tvalid_0's l1: 0.0763776\tvalid_0's l2: 0.0105204\n",
      "[23]\tvalid_0's l1: 0.074906\tvalid_0's l2: 0.010265\n",
      "[24]\tvalid_0's l1: 0.0732985\tvalid_0's l2: 0.00994854\n",
      "[25]\tvalid_0's l1: 0.0720481\tvalid_0's l2: 0.00970346\n",
      "[26]\tvalid_0's l1: 0.0710837\tvalid_0's l2: 0.00953733\n",
      "[27]\tvalid_0's l1: 0.0703066\tvalid_0's l2: 0.00939487\n",
      "[28]\tvalid_0's l1: 0.0694075\tvalid_0's l2: 0.009231\n",
      "[29]\tvalid_0's l1: 0.0685866\tvalid_0's l2: 0.00907982\n",
      "[30]\tvalid_0's l1: 0.06779\tvalid_0's l2: 0.0089391\n",
      "[31]\tvalid_0's l1: 0.067074\tvalid_0's l2: 0.00880224\n",
      "[32]\tvalid_0's l1: 0.0665149\tvalid_0's l2: 0.00871073\n",
      "[33]\tvalid_0's l1: 0.0659205\tvalid_0's l2: 0.00858992\n",
      "[34]\tvalid_0's l1: 0.0652426\tvalid_0's l2: 0.00843778\n",
      "[35]\tvalid_0's l1: 0.0648768\tvalid_0's l2: 0.00836372\n",
      "[36]\tvalid_0's l1: 0.0645306\tvalid_0's l2: 0.00830614\n",
      "[37]\tvalid_0's l1: 0.0640175\tvalid_0's l2: 0.00818645\n",
      "[38]\tvalid_0's l1: 0.0637947\tvalid_0's l2: 0.00814636\n",
      "[39]\tvalid_0's l1: 0.0635605\tvalid_0's l2: 0.00810447\n",
      "[40]\tvalid_0's l1: 0.0633449\tvalid_0's l2: 0.00806796\n",
      "[41]\tvalid_0's l1: 0.0631446\tvalid_0's l2: 0.00802436\n",
      "[42]\tvalid_0's l1: 0.0627575\tvalid_0's l2: 0.00792715\n",
      "[43]\tvalid_0's l1: 0.062611\tvalid_0's l2: 0.00789809\n",
      "[44]\tvalid_0's l1: 0.0623997\tvalid_0's l2: 0.00785422\n",
      "[45]\tvalid_0's l1: 0.0622728\tvalid_0's l2: 0.00783369\n",
      "[46]\tvalid_0's l1: 0.0621336\tvalid_0's l2: 0.00781042\n",
      "[47]\tvalid_0's l1: 0.0619906\tvalid_0's l2: 0.00777463\n",
      "[48]\tvalid_0's l1: 0.0618556\tvalid_0's l2: 0.00774471\n",
      "[49]\tvalid_0's l1: 0.0617449\tvalid_0's l2: 0.00772713\n",
      "[50]\tvalid_0's l1: 0.0616454\tvalid_0's l2: 0.00770345\n",
      "[51]\tvalid_0's l1: 0.0615492\tvalid_0's l2: 0.00768433\n",
      "[52]\tvalid_0's l1: 0.0614452\tvalid_0's l2: 0.00766572\n",
      "[53]\tvalid_0's l1: 0.0613725\tvalid_0's l2: 0.00764408\n",
      "[54]\tvalid_0's l1: 0.0612796\tvalid_0's l2: 0.00762834\n",
      "[55]\tvalid_0's l1: 0.0611706\tvalid_0's l2: 0.00760066\n",
      "[56]\tvalid_0's l1: 0.0611121\tvalid_0's l2: 0.00759012\n",
      "[57]\tvalid_0's l1: 0.0610472\tvalid_0's l2: 0.00757864\n",
      "[58]\tvalid_0's l1: 0.0609801\tvalid_0's l2: 0.00756664\n",
      "[59]\tvalid_0's l1: 0.0608993\tvalid_0's l2: 0.00755207\n",
      "[60]\tvalid_0's l1: 0.0608206\tvalid_0's l2: 0.00753343\n",
      "[61]\tvalid_0's l1: 0.0607751\tvalid_0's l2: 0.00752435\n",
      "[62]\tvalid_0's l1: 0.0607163\tvalid_0's l2: 0.00751128\n",
      "[63]\tvalid_0's l1: 0.0605746\tvalid_0's l2: 0.00747752\n",
      "[64]\tvalid_0's l1: 0.0604651\tvalid_0's l2: 0.0074507\n",
      "[65]\tvalid_0's l1: 0.0604263\tvalid_0's l2: 0.00744162\n",
      "[66]\tvalid_0's l1: 0.0603599\tvalid_0's l2: 0.00742889\n",
      "[67]\tvalid_0's l1: 0.0602701\tvalid_0's l2: 0.00741169\n",
      "[68]\tvalid_0's l1: 0.0602334\tvalid_0's l2: 0.00739977\n",
      "[69]\tvalid_0's l1: 0.060112\tvalid_0's l2: 0.007374\n",
      "[70]\tvalid_0's l1: 0.0600257\tvalid_0's l2: 0.00735234\n",
      "[71]\tvalid_0's l1: 0.059928\tvalid_0's l2: 0.00732775\n",
      "[72]\tvalid_0's l1: 0.0598568\tvalid_0's l2: 0.00731406\n",
      "[73]\tvalid_0's l1: 0.0598019\tvalid_0's l2: 0.007307\n",
      "[74]\tvalid_0's l1: 0.0597175\tvalid_0's l2: 0.00728248\n",
      "[75]\tvalid_0's l1: 0.0596246\tvalid_0's l2: 0.00726385\n",
      "[76]\tvalid_0's l1: 0.0596029\tvalid_0's l2: 0.00725546\n",
      "[77]\tvalid_0's l1: 0.0595231\tvalid_0's l2: 0.00723235\n",
      "[78]\tvalid_0's l1: 0.0595014\tvalid_0's l2: 0.00722831\n",
      "[79]\tvalid_0's l1: 0.0594712\tvalid_0's l2: 0.00722155\n",
      "[80]\tvalid_0's l1: 0.0594512\tvalid_0's l2: 0.00721585\n",
      "[81]\tvalid_0's l1: 0.0594197\tvalid_0's l2: 0.00720856\n",
      "[82]\tvalid_0's l1: 0.0593932\tvalid_0's l2: 0.00720475\n",
      "[83]\tvalid_0's l1: 0.0593157\tvalid_0's l2: 0.00718559\n",
      "[84]\tvalid_0's l1: 0.0592926\tvalid_0's l2: 0.00718174\n",
      "[85]\tvalid_0's l1: 0.0592627\tvalid_0's l2: 0.00717558\n",
      "[86]\tvalid_0's l1: 0.0592466\tvalid_0's l2: 0.00716984\n",
      "[87]\tvalid_0's l1: 0.0592043\tvalid_0's l2: 0.00716193\n",
      "[88]\tvalid_0's l1: 0.0591298\tvalid_0's l2: 0.00714342\n",
      "[89]\tvalid_0's l1: 0.059097\tvalid_0's l2: 0.00713607\n",
      "[90]\tvalid_0's l1: 0.0589982\tvalid_0's l2: 0.00711745\n",
      "[91]\tvalid_0's l1: 0.0589505\tvalid_0's l2: 0.00710413\n",
      "[92]\tvalid_0's l1: 0.0588884\tvalid_0's l2: 0.00708708\n",
      "[93]\tvalid_0's l1: 0.0588084\tvalid_0's l2: 0.00707062\n",
      "[94]\tvalid_0's l1: 0.0587967\tvalid_0's l2: 0.00707269\n",
      "[95]\tvalid_0's l1: 0.0587689\tvalid_0's l2: 0.00706628\n",
      "[96]\tvalid_0's l1: 0.058749\tvalid_0's l2: 0.00705746\n",
      "[97]\tvalid_0's l1: 0.0587121\tvalid_0's l2: 0.00704977\n",
      "[98]\tvalid_0's l1: 0.0586712\tvalid_0's l2: 0.00703874\n",
      "[99]\tvalid_0's l1: 0.05861\tvalid_0's l2: 0.0070237\n",
      "[100]\tvalid_0's l1: 0.0585889\tvalid_0's l2: 0.00701449\n",
      "[101]\tvalid_0's l1: 0.0585771\tvalid_0's l2: 0.00701003\n",
      "[102]\tvalid_0's l1: 0.0585635\tvalid_0's l2: 0.0070058\n",
      "[103]\tvalid_0's l1: 0.0585479\tvalid_0's l2: 0.00700123\n",
      "[104]\tvalid_0's l1: 0.0585384\tvalid_0's l2: 0.00699934\n",
      "[105]\tvalid_0's l1: 0.0585209\tvalid_0's l2: 0.00699689\n",
      "[106]\tvalid_0's l1: 0.0585063\tvalid_0's l2: 0.00699365\n",
      "[107]\tvalid_0's l1: 0.0584905\tvalid_0's l2: 0.00698833\n",
      "[108]\tvalid_0's l1: 0.0584738\tvalid_0's l2: 0.00698361\n",
      "[109]\tvalid_0's l1: 0.0584563\tvalid_0's l2: 0.00697852\n",
      "[110]\tvalid_0's l1: 0.0584379\tvalid_0's l2: 0.00697721\n",
      "[111]\tvalid_0's l1: 0.0584333\tvalid_0's l2: 0.00697385\n",
      "[112]\tvalid_0's l1: 0.0584316\tvalid_0's l2: 0.00697371\n",
      "[113]\tvalid_0's l1: 0.0584076\tvalid_0's l2: 0.00696515\n",
      "[114]\tvalid_0's l1: 0.0583903\tvalid_0's l2: 0.00696145\n",
      "[115]\tvalid_0's l1: 0.0583817\tvalid_0's l2: 0.00695705\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[116]\tvalid_0's l1: 0.0583585\tvalid_0's l2: 0.00695171\n",
      "[117]\tvalid_0's l1: 0.0583329\tvalid_0's l2: 0.00694439\n",
      "[118]\tvalid_0's l1: 0.0582953\tvalid_0's l2: 0.00693451\n",
      "[119]\tvalid_0's l1: 0.0582302\tvalid_0's l2: 0.00691471\n",
      "[120]\tvalid_0's l1: 0.0582053\tvalid_0's l2: 0.00690717\n",
      "[121]\tvalid_0's l1: 0.058165\tvalid_0's l2: 0.00690191\n",
      "[122]\tvalid_0's l1: 0.0581507\tvalid_0's l2: 0.00689919\n",
      "[123]\tvalid_0's l1: 0.0581202\tvalid_0's l2: 0.00689344\n",
      "[124]\tvalid_0's l1: 0.0580842\tvalid_0's l2: 0.00688742\n",
      "[125]\tvalid_0's l1: 0.0580695\tvalid_0's l2: 0.00688381\n",
      "[126]\tvalid_0's l1: 0.0580556\tvalid_0's l2: 0.0068812\n",
      "[127]\tvalid_0's l1: 0.0580404\tvalid_0's l2: 0.00687439\n",
      "[128]\tvalid_0's l1: 0.0580276\tvalid_0's l2: 0.00687128\n",
      "[129]\tvalid_0's l1: 0.0579982\tvalid_0's l2: 0.00686654\n",
      "[130]\tvalid_0's l1: 0.0579886\tvalid_0's l2: 0.00686163\n",
      "[131]\tvalid_0's l1: 0.0579795\tvalid_0's l2: 0.00686265\n",
      "[132]\tvalid_0's l1: 0.0579773\tvalid_0's l2: 0.00686113\n",
      "[133]\tvalid_0's l1: 0.0579718\tvalid_0's l2: 0.00686023\n",
      "[134]\tvalid_0's l1: 0.0579558\tvalid_0's l2: 0.00685794\n",
      "[135]\tvalid_0's l1: 0.0579452\tvalid_0's l2: 0.00685478\n",
      "[136]\tvalid_0's l1: 0.0579375\tvalid_0's l2: 0.00685219\n",
      "[137]\tvalid_0's l1: 0.0579311\tvalid_0's l2: 0.00685004\n",
      "[138]\tvalid_0's l1: 0.0578929\tvalid_0's l2: 0.0068395\n",
      "[139]\tvalid_0's l1: 0.0578863\tvalid_0's l2: 0.00683737\n",
      "[140]\tvalid_0's l1: 0.0578618\tvalid_0's l2: 0.00682973\n",
      "[141]\tvalid_0's l1: 0.057814\tvalid_0's l2: 0.00682006\n",
      "[142]\tvalid_0's l1: 0.0577865\tvalid_0's l2: 0.00681178\n",
      "[143]\tvalid_0's l1: 0.0577836\tvalid_0's l2: 0.00681092\n",
      "[144]\tvalid_0's l1: 0.0577732\tvalid_0's l2: 0.00680807\n",
      "[145]\tvalid_0's l1: 0.05775\tvalid_0's l2: 0.00680476\n",
      "[146]\tvalid_0's l1: 0.0577295\tvalid_0's l2: 0.00679983\n",
      "[147]\tvalid_0's l1: 0.0577274\tvalid_0's l2: 0.00679906\n",
      "[148]\tvalid_0's l1: 0.0577282\tvalid_0's l2: 0.00680071\n",
      "[149]\tvalid_0's l1: 0.0577027\tvalid_0's l2: 0.00679154\n",
      "[150]\tvalid_0's l1: 0.0577023\tvalid_0's l2: 0.00679031\n",
      "[151]\tvalid_0's l1: 0.0576895\tvalid_0's l2: 0.0067869\n",
      "[152]\tvalid_0's l1: 0.0576863\tvalid_0's l2: 0.00678669\n",
      "[153]\tvalid_0's l1: 0.057681\tvalid_0's l2: 0.00678298\n",
      "[154]\tvalid_0's l1: 0.0576461\tvalid_0's l2: 0.00677587\n",
      "[155]\tvalid_0's l1: 0.0576281\tvalid_0's l2: 0.00676922\n",
      "[156]\tvalid_0's l1: 0.0576071\tvalid_0's l2: 0.00676582\n",
      "[157]\tvalid_0's l1: 0.0575831\tvalid_0's l2: 0.00676433\n",
      "[158]\tvalid_0's l1: 0.0575486\tvalid_0's l2: 0.00675594\n",
      "[159]\tvalid_0's l1: 0.0575371\tvalid_0's l2: 0.00675428\n",
      "[160]\tvalid_0's l1: 0.0575191\tvalid_0's l2: 0.00675119\n",
      "[161]\tvalid_0's l1: 0.0575124\tvalid_0's l2: 0.00675132\n",
      "[162]\tvalid_0's l1: 0.0575015\tvalid_0's l2: 0.00674851\n",
      "[163]\tvalid_0's l1: 0.0574785\tvalid_0's l2: 0.00674612\n",
      "[164]\tvalid_0's l1: 0.0574678\tvalid_0's l2: 0.00674357\n",
      "[165]\tvalid_0's l1: 0.0574633\tvalid_0's l2: 0.00674366\n",
      "[166]\tvalid_0's l1: 0.0574458\tvalid_0's l2: 0.00673905\n",
      "[167]\tvalid_0's l1: 0.0574112\tvalid_0's l2: 0.00672946\n",
      "[168]\tvalid_0's l1: 0.0573985\tvalid_0's l2: 0.00672684\n",
      "[169]\tvalid_0's l1: 0.0573967\tvalid_0's l2: 0.00672339\n",
      "[170]\tvalid_0's l1: 0.0573916\tvalid_0's l2: 0.00672143\n",
      "[171]\tvalid_0's l1: 0.0573821\tvalid_0's l2: 0.00671896\n",
      "[172]\tvalid_0's l1: 0.0573703\tvalid_0's l2: 0.00671675\n",
      "[173]\tvalid_0's l1: 0.0573778\tvalid_0's l2: 0.00671835\n",
      "[174]\tvalid_0's l1: 0.0573707\tvalid_0's l2: 0.00671666\n",
      "[175]\tvalid_0's l1: 0.0573572\tvalid_0's l2: 0.00671479\n",
      "[176]\tvalid_0's l1: 0.0573574\tvalid_0's l2: 0.0067135\n",
      "[177]\tvalid_0's l1: 0.0573542\tvalid_0's l2: 0.00671297\n",
      "[178]\tvalid_0's l1: 0.057355\tvalid_0's l2: 0.00671458\n",
      "[179]\tvalid_0's l1: 0.0573403\tvalid_0's l2: 0.00671148\n",
      "[180]\tvalid_0's l1: 0.0573278\tvalid_0's l2: 0.00670699\n",
      "[181]\tvalid_0's l1: 0.0573277\tvalid_0's l2: 0.00670656\n",
      "[182]\tvalid_0's l1: 0.0573192\tvalid_0's l2: 0.00670432\n",
      "[183]\tvalid_0's l1: 0.0573012\tvalid_0's l2: 0.00670044\n",
      "[184]\tvalid_0's l1: 0.0573018\tvalid_0's l2: 0.00669917\n",
      "[185]\tvalid_0's l1: 0.0572933\tvalid_0's l2: 0.00669791\n",
      "[186]\tvalid_0's l1: 0.0572937\tvalid_0's l2: 0.00669628\n",
      "[187]\tvalid_0's l1: 0.0572959\tvalid_0's l2: 0.006696\n",
      "[188]\tvalid_0's l1: 0.0572946\tvalid_0's l2: 0.00669529\n",
      "[189]\tvalid_0's l1: 0.057284\tvalid_0's l2: 0.00669419\n",
      "[190]\tvalid_0's l1: 0.0572756\tvalid_0's l2: 0.00669183\n",
      "[191]\tvalid_0's l1: 0.0572536\tvalid_0's l2: 0.00668716\n",
      "[192]\tvalid_0's l1: 0.0572549\tvalid_0's l2: 0.0066889\n",
      "[193]\tvalid_0's l1: 0.0572407\tvalid_0's l2: 0.00668565\n",
      "[194]\tvalid_0's l1: 0.0572137\tvalid_0's l2: 0.00668092\n",
      "[195]\tvalid_0's l1: 0.0572176\tvalid_0's l2: 0.00668088\n",
      "[196]\tvalid_0's l1: 0.0571987\tvalid_0's l2: 0.00667881\n",
      "[197]\tvalid_0's l1: 0.0571904\tvalid_0's l2: 0.00667394\n",
      "[198]\tvalid_0's l1: 0.0571825\tvalid_0's l2: 0.00667086\n",
      "[199]\tvalid_0's l1: 0.0571814\tvalid_0's l2: 0.0066715\n",
      "[200]\tvalid_0's l1: 0.0571754\tvalid_0's l2: 0.0066712\n",
      "[201]\tvalid_0's l1: 0.0571679\tvalid_0's l2: 0.00666813\n",
      "[202]\tvalid_0's l1: 0.0571538\tvalid_0's l2: 0.00666858\n",
      "[203]\tvalid_0's l1: 0.0571505\tvalid_0's l2: 0.00666737\n",
      "[204]\tvalid_0's l1: 0.0571496\tvalid_0's l2: 0.00666642\n",
      "[205]\tvalid_0's l1: 0.0571438\tvalid_0's l2: 0.00666369\n",
      "[206]\tvalid_0's l1: 0.0571522\tvalid_0's l2: 0.0066647\n",
      "[207]\tvalid_0's l1: 0.0571443\tvalid_0's l2: 0.00666353\n",
      "[208]\tvalid_0's l1: 0.057147\tvalid_0's l2: 0.00666341\n",
      "[209]\tvalid_0's l1: 0.0571413\tvalid_0's l2: 0.00666194\n",
      "[210]\tvalid_0's l1: 0.0571264\tvalid_0's l2: 0.00666014\n",
      "[211]\tvalid_0's l1: 0.0571122\tvalid_0's l2: 0.00665585\n",
      "[212]\tvalid_0's l1: 0.0571118\tvalid_0's l2: 0.00665518\n",
      "[213]\tvalid_0's l1: 0.0571002\tvalid_0's l2: 0.00665338\n",
      "[214]\tvalid_0's l1: 0.0570964\tvalid_0's l2: 0.00665241\n",
      "[215]\tvalid_0's l1: 0.05709\tvalid_0's l2: 0.00665055\n",
      "[216]\tvalid_0's l1: 0.0570856\tvalid_0's l2: 0.00665057\n",
      "[217]\tvalid_0's l1: 0.0570826\tvalid_0's l2: 0.00665004\n",
      "[218]\tvalid_0's l1: 0.0570747\tvalid_0's l2: 0.00664898\n",
      "[219]\tvalid_0's l1: 0.057068\tvalid_0's l2: 0.00664791\n",
      "[220]\tvalid_0's l1: 0.05707\tvalid_0's l2: 0.00664691\n",
      "[221]\tvalid_0's l1: 0.0570675\tvalid_0's l2: 0.00664523\n",
      "[222]\tvalid_0's l1: 0.0570426\tvalid_0's l2: 0.00664251\n",
      "[223]\tvalid_0's l1: 0.0570167\tvalid_0's l2: 0.00663508\n",
      "[224]\tvalid_0's l1: 0.0570101\tvalid_0's l2: 0.00663261\n",
      "[225]\tvalid_0's l1: 0.0570103\tvalid_0's l2: 0.00663457\n",
      "[226]\tvalid_0's l1: 0.0569972\tvalid_0's l2: 0.00663136\n",
      "[227]\tvalid_0's l1: 0.0569882\tvalid_0's l2: 0.00663009\n",
      "[228]\tvalid_0's l1: 0.0569898\tvalid_0's l2: 0.00662954\n",
      "[229]\tvalid_0's l1: 0.0569865\tvalid_0's l2: 0.00662705\n",
      "[230]\tvalid_0's l1: 0.0569757\tvalid_0's l2: 0.00662507\n",
      "[231]\tvalid_0's l1: 0.0569731\tvalid_0's l2: 0.00662383\n",
      "[232]\tvalid_0's l1: 0.0569676\tvalid_0's l2: 0.00662384\n",
      "[233]\tvalid_0's l1: 0.0569664\tvalid_0's l2: 0.00662459\n",
      "[234]\tvalid_0's l1: 0.0569611\tvalid_0's l2: 0.00662457\n",
      "[235]\tvalid_0's l1: 0.0569633\tvalid_0's l2: 0.00662413\n",
      "[236]\tvalid_0's l1: 0.0569615\tvalid_0's l2: 0.00662337\n",
      "[237]\tvalid_0's l1: 0.0569616\tvalid_0's l2: 0.00662352\n",
      "[238]\tvalid_0's l1: 0.0569541\tvalid_0's l2: 0.00662248\n",
      "[239]\tvalid_0's l1: 0.0569478\tvalid_0's l2: 0.0066219\n",
      "[240]\tvalid_0's l1: 0.0569487\tvalid_0's l2: 0.00662202\n",
      "[241]\tvalid_0's l1: 0.0569517\tvalid_0's l2: 0.00662222\n",
      "[242]\tvalid_0's l1: 0.0569443\tvalid_0's l2: 0.00662104\n",
      "[243]\tvalid_0's l1: 0.0569249\tvalid_0's l2: 0.00661558\n",
      "[244]\tvalid_0's l1: 0.0569245\tvalid_0's l2: 0.00661384\n",
      "[245]\tvalid_0's l1: 0.0569247\tvalid_0's l2: 0.00661414\n",
      "[246]\tvalid_0's l1: 0.0569229\tvalid_0's l2: 0.00661358\n",
      "[247]\tvalid_0's l1: 0.0569195\tvalid_0's l2: 0.00661233\n",
      "[248]\tvalid_0's l1: 0.0569077\tvalid_0's l2: 0.00660972\n",
      "[249]\tvalid_0's l1: 0.0569058\tvalid_0's l2: 0.00660907\n",
      "[250]\tvalid_0's l1: 0.0569022\tvalid_0's l2: 0.00660765\n",
      "[251]\tvalid_0's l1: 0.056903\tvalid_0's l2: 0.00660776\n",
      "[252]\tvalid_0's l1: 0.0569011\tvalid_0's l2: 0.00660744\n",
      "[253]\tvalid_0's l1: 0.0569056\tvalid_0's l2: 0.00660932\n",
      "[254]\tvalid_0's l1: 0.0569014\tvalid_0's l2: 0.00660959\n",
      "[255]\tvalid_0's l1: 0.0568977\tvalid_0's l2: 0.00660912\n",
      "[256]\tvalid_0's l1: 0.0568991\tvalid_0's l2: 0.00660947\n",
      "[257]\tvalid_0's l1: 0.0568659\tvalid_0's l2: 0.00660135\n",
      "[258]\tvalid_0's l1: 0.0568594\tvalid_0's l2: 0.00660015\n",
      "[259]\tvalid_0's l1: 0.0568588\tvalid_0's l2: 0.00659937\n",
      "[260]\tvalid_0's l1: 0.0568588\tvalid_0's l2: 0.00659942\n",
      "[261]\tvalid_0's l1: 0.056858\tvalid_0's l2: 0.00659948\n",
      "[262]\tvalid_0's l1: 0.0568521\tvalid_0's l2: 0.00659886\n",
      "[263]\tvalid_0's l1: 0.0568516\tvalid_0's l2: 0.00659866\n",
      "[264]\tvalid_0's l1: 0.0568237\tvalid_0's l2: 0.00659403\n",
      "[265]\tvalid_0's l1: 0.0568185\tvalid_0's l2: 0.00659247\n",
      "[266]\tvalid_0's l1: 0.0568207\tvalid_0's l2: 0.00659355\n",
      "[267]\tvalid_0's l1: 0.0568153\tvalid_0's l2: 0.00659291\n",
      "[268]\tvalid_0's l1: 0.0568104\tvalid_0's l2: 0.00659252\n",
      "[269]\tvalid_0's l1: 0.0568035\tvalid_0's l2: 0.00659123\n",
      "[270]\tvalid_0's l1: 0.0567872\tvalid_0's l2: 0.00658465\n",
      "[271]\tvalid_0's l1: 0.0567721\tvalid_0's l2: 0.00658252\n",
      "[272]\tvalid_0's l1: 0.0567615\tvalid_0's l2: 0.00658002\n",
      "[273]\tvalid_0's l1: 0.0567417\tvalid_0's l2: 0.00657648\n",
      "[274]\tvalid_0's l1: 0.0567421\tvalid_0's l2: 0.00657556\n",
      "[275]\tvalid_0's l1: 0.0567452\tvalid_0's l2: 0.00657642\n",
      "[276]\tvalid_0's l1: 0.0567469\tvalid_0's l2: 0.00657607\n",
      "[277]\tvalid_0's l1: 0.056743\tvalid_0's l2: 0.00657593\n",
      "[278]\tvalid_0's l1: 0.0567391\tvalid_0's l2: 0.00657495\n",
      "[279]\tvalid_0's l1: 0.056739\tvalid_0's l2: 0.00657478\n",
      "[280]\tvalid_0's l1: 0.0567325\tvalid_0's l2: 0.00657337\n",
      "[281]\tvalid_0's l1: 0.0567323\tvalid_0's l2: 0.00657334\n",
      "[282]\tvalid_0's l1: 0.0567366\tvalid_0's l2: 0.00657365\n",
      "[283]\tvalid_0's l1: 0.0567364\tvalid_0's l2: 0.00657325\n",
      "[284]\tvalid_0's l1: 0.0567233\tvalid_0's l2: 0.00657079\n",
      "[285]\tvalid_0's l1: 0.0567246\tvalid_0's l2: 0.0065718\n",
      "[286]\tvalid_0's l1: 0.0567265\tvalid_0's l2: 0.00657222\n",
      "[287]\tvalid_0's l1: 0.0567223\tvalid_0's l2: 0.00657141\n",
      "[288]\tvalid_0's l1: 0.056714\tvalid_0's l2: 0.00656963\n",
      "[289]\tvalid_0's l1: 0.0567092\tvalid_0's l2: 0.00656967\n",
      "[290]\tvalid_0's l1: 0.0566989\tvalid_0's l2: 0.00656775\n",
      "[291]\tvalid_0's l1: 0.0566969\tvalid_0's l2: 0.00656714\n",
      "[292]\tvalid_0's l1: 0.0566972\tvalid_0's l2: 0.00656797\n",
      "[293]\tvalid_0's l1: 0.0566986\tvalid_0's l2: 0.00656812\n",
      "[294]\tvalid_0's l1: 0.056693\tvalid_0's l2: 0.00656709\n",
      "[295]\tvalid_0's l1: 0.0566943\tvalid_0's l2: 0.00656743\n",
      "[296]\tvalid_0's l1: 0.0566987\tvalid_0's l2: 0.0065679\n",
      "[297]\tvalid_0's l1: 0.056698\tvalid_0's l2: 0.00656738\n",
      "[298]\tvalid_0's l1: 0.0566927\tvalid_0's l2: 0.00656645\n",
      "[299]\tvalid_0's l1: 0.0566939\tvalid_0's l2: 0.00656589\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[300]\tvalid_0's l1: 0.0566834\tvalid_0's l2: 0.00656346\n",
      "[301]\tvalid_0's l1: 0.0566872\tvalid_0's l2: 0.0065663\n",
      "[302]\tvalid_0's l1: 0.0566856\tvalid_0's l2: 0.00656487\n",
      "[303]\tvalid_0's l1: 0.0566822\tvalid_0's l2: 0.00656261\n",
      "[304]\tvalid_0's l1: 0.0566848\tvalid_0's l2: 0.00656279\n",
      "[305]\tvalid_0's l1: 0.0566818\tvalid_0's l2: 0.00656087\n",
      "[306]\tvalid_0's l1: 0.0566818\tvalid_0's l2: 0.00656023\n",
      "[307]\tvalid_0's l1: 0.0566825\tvalid_0's l2: 0.00655957\n",
      "[308]\tvalid_0's l1: 0.0566804\tvalid_0's l2: 0.00655989\n",
      "[309]\tvalid_0's l1: 0.0566693\tvalid_0's l2: 0.00655513\n",
      "[310]\tvalid_0's l1: 0.0566608\tvalid_0's l2: 0.00655397\n",
      "[311]\tvalid_0's l1: 0.0566546\tvalid_0's l2: 0.00655309\n",
      "[312]\tvalid_0's l1: 0.0566519\tvalid_0's l2: 0.00655274\n",
      "[313]\tvalid_0's l1: 0.05665\tvalid_0's l2: 0.00655275\n",
      "[314]\tvalid_0's l1: 0.0566492\tvalid_0's l2: 0.00655257\n",
      "[315]\tvalid_0's l1: 0.0566516\tvalid_0's l2: 0.00655293\n",
      "[316]\tvalid_0's l1: 0.0566499\tvalid_0's l2: 0.00655212\n",
      "[317]\tvalid_0's l1: 0.0566385\tvalid_0's l2: 0.00655\n",
      "[318]\tvalid_0's l1: 0.0566288\tvalid_0's l2: 0.00654956\n",
      "[319]\tvalid_0's l1: 0.0566317\tvalid_0's l2: 0.00655049\n",
      "[320]\tvalid_0's l1: 0.056631\tvalid_0's l2: 0.00655062\n",
      "[321]\tvalid_0's l1: 0.0566272\tvalid_0's l2: 0.00654966\n",
      "[322]\tvalid_0's l1: 0.0566247\tvalid_0's l2: 0.00654857\n",
      "[323]\tvalid_0's l1: 0.0566318\tvalid_0's l2: 0.00654932\n",
      "[324]\tvalid_0's l1: 0.0566344\tvalid_0's l2: 0.00655044\n",
      "[325]\tvalid_0's l1: 0.0566239\tvalid_0's l2: 0.00654858\n",
      "[326]\tvalid_0's l1: 0.0566241\tvalid_0's l2: 0.00654811\n",
      "[327]\tvalid_0's l1: 0.056621\tvalid_0's l2: 0.00654755\n",
      "[328]\tvalid_0's l1: 0.056627\tvalid_0's l2: 0.00655085\n",
      "[329]\tvalid_0's l1: 0.0566305\tvalid_0's l2: 0.00655121\n",
      "[330]\tvalid_0's l1: 0.0566281\tvalid_0's l2: 0.00655078\n",
      "[331]\tvalid_0's l1: 0.0566228\tvalid_0's l2: 0.00655045\n",
      "[332]\tvalid_0's l1: 0.0566242\tvalid_0's l2: 0.00654992\n",
      "Early stopping, best iteration is:\n",
      "[327]\tvalid_0's l1: 0.056621\tvalid_0's l2: 0.00654755\n",
      "本次结果输出的mae值是:\n",
      " 0.0566209902947507\n",
      "[1]\tvalid_0's l1: 0.244105\tvalid_0's l2: 0.0788929\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[2]\tvalid_0's l1: 0.223433\tvalid_0's l2: 0.0665048\n",
      "[3]\tvalid_0's l1: 0.204693\tvalid_0's l2: 0.056241\n",
      "[4]\tvalid_0's l1: 0.188138\tvalid_0's l2: 0.0479084\n",
      "[5]\tvalid_0's l1: 0.173652\tvalid_0's l2: 0.0411735\n",
      "[6]\tvalid_0's l1: 0.160452\tvalid_0's l2: 0.0355248\n",
      "[7]\tvalid_0's l1: 0.149048\tvalid_0's l2: 0.0309893\n",
      "[8]\tvalid_0's l1: 0.138672\tvalid_0's l2: 0.0271575\n",
      "[9]\tvalid_0's l1: 0.129588\tvalid_0's l2: 0.0240739\n",
      "[10]\tvalid_0's l1: 0.12149\tvalid_0's l2: 0.0214714\n",
      "[11]\tvalid_0's l1: 0.11444\tvalid_0's l2: 0.0193639\n",
      "[12]\tvalid_0's l1: 0.108472\tvalid_0's l2: 0.0177015\n",
      "[13]\tvalid_0's l1: 0.102859\tvalid_0's l2: 0.0162138\n",
      "[14]\tvalid_0's l1: 0.0979989\tvalid_0's l2: 0.0149889\n",
      "[15]\tvalid_0's l1: 0.093631\tvalid_0's l2: 0.0139367\n",
      "[16]\tvalid_0's l1: 0.0897701\tvalid_0's l2: 0.0130511\n",
      "[17]\tvalid_0's l1: 0.0866436\tvalid_0's l2: 0.0123709\n",
      "[18]\tvalid_0's l1: 0.0837132\tvalid_0's l2: 0.011743\n",
      "[19]\tvalid_0's l1: 0.081271\tvalid_0's l2: 0.0112436\n",
      "[20]\tvalid_0's l1: 0.0787173\tvalid_0's l2: 0.0106819\n",
      "[21]\tvalid_0's l1: 0.0765278\tvalid_0's l2: 0.0102272\n",
      "[22]\tvalid_0's l1: 0.0747217\tvalid_0's l2: 0.00988702\n",
      "[23]\tvalid_0's l1: 0.0732441\tvalid_0's l2: 0.009621\n",
      "[24]\tvalid_0's l1: 0.0719183\tvalid_0's l2: 0.00938525\n",
      "[25]\tvalid_0's l1: 0.0706699\tvalid_0's l2: 0.00915448\n",
      "[26]\tvalid_0's l1: 0.0697174\tvalid_0's l2: 0.0089885\n",
      "[27]\tvalid_0's l1: 0.068879\tvalid_0's l2: 0.00884727\n",
      "[28]\tvalid_0's l1: 0.067942\tvalid_0's l2: 0.00866247\n",
      "[29]\tvalid_0's l1: 0.0673086\tvalid_0's l2: 0.00855939\n",
      "[30]\tvalid_0's l1: 0.0666173\tvalid_0's l2: 0.0084259\n",
      "[31]\tvalid_0's l1: 0.0659214\tvalid_0's l2: 0.00829166\n",
      "[32]\tvalid_0's l1: 0.065314\tvalid_0's l2: 0.00818122\n",
      "[33]\tvalid_0's l1: 0.0648871\tvalid_0's l2: 0.00811447\n",
      "[34]\tvalid_0's l1: 0.064382\tvalid_0's l2: 0.00802105\n",
      "[35]\tvalid_0's l1: 0.0640183\tvalid_0's l2: 0.00795961\n",
      "[36]\tvalid_0's l1: 0.0636983\tvalid_0's l2: 0.0079045\n",
      "[37]\tvalid_0's l1: 0.0633309\tvalid_0's l2: 0.00783605\n",
      "[38]\tvalid_0's l1: 0.0630379\tvalid_0's l2: 0.00777941\n",
      "[39]\tvalid_0's l1: 0.0627646\tvalid_0's l2: 0.00773424\n",
      "[40]\tvalid_0's l1: 0.0625134\tvalid_0's l2: 0.00769552\n",
      "[41]\tvalid_0's l1: 0.0622952\tvalid_0's l2: 0.00765205\n",
      "[42]\tvalid_0's l1: 0.0621023\tvalid_0's l2: 0.00761986\n",
      "[43]\tvalid_0's l1: 0.0617965\tvalid_0's l2: 0.00755124\n",
      "[44]\tvalid_0's l1: 0.0616577\tvalid_0's l2: 0.0075282\n",
      "[45]\tvalid_0's l1: 0.0614919\tvalid_0's l2: 0.00749925\n",
      "[46]\tvalid_0's l1: 0.0613431\tvalid_0's l2: 0.00747765\n",
      "[47]\tvalid_0's l1: 0.0610837\tvalid_0's l2: 0.00742058\n",
      "[48]\tvalid_0's l1: 0.0609448\tvalid_0's l2: 0.00739924\n",
      "[49]\tvalid_0's l1: 0.0608516\tvalid_0's l2: 0.00738553\n",
      "[50]\tvalid_0's l1: 0.0606495\tvalid_0's l2: 0.00733807\n",
      "[51]\tvalid_0's l1: 0.060549\tvalid_0's l2: 0.00731766\n",
      "[52]\tvalid_0's l1: 0.0604562\tvalid_0's l2: 0.0073052\n",
      "[53]\tvalid_0's l1: 0.0603682\tvalid_0's l2: 0.00729467\n",
      "[54]\tvalid_0's l1: 0.0602269\tvalid_0's l2: 0.00725959\n",
      "[55]\tvalid_0's l1: 0.060151\tvalid_0's l2: 0.00724768\n",
      "[56]\tvalid_0's l1: 0.060094\tvalid_0's l2: 0.00723379\n",
      "[57]\tvalid_0's l1: 0.059947\tvalid_0's l2: 0.00720518\n",
      "[58]\tvalid_0's l1: 0.0598583\tvalid_0's l2: 0.00719324\n",
      "[59]\tvalid_0's l1: 0.0597802\tvalid_0's l2: 0.00717468\n",
      "[60]\tvalid_0's l1: 0.0596954\tvalid_0's l2: 0.00715649\n",
      "[61]\tvalid_0's l1: 0.0596543\tvalid_0's l2: 0.00714967\n",
      "[62]\tvalid_0's l1: 0.0596053\tvalid_0's l2: 0.00714439\n",
      "[63]\tvalid_0's l1: 0.0594822\tvalid_0's l2: 0.00712291\n",
      "[64]\tvalid_0's l1: 0.0594105\tvalid_0's l2: 0.00710717\n",
      "[65]\tvalid_0's l1: 0.0593536\tvalid_0's l2: 0.00709356\n",
      "[66]\tvalid_0's l1: 0.0593072\tvalid_0's l2: 0.00708164\n",
      "[67]\tvalid_0's l1: 0.0592132\tvalid_0's l2: 0.00706076\n",
      "[68]\tvalid_0's l1: 0.0591506\tvalid_0's l2: 0.00704672\n",
      "[69]\tvalid_0's l1: 0.0591161\tvalid_0's l2: 0.00704004\n",
      "[70]\tvalid_0's l1: 0.0590643\tvalid_0's l2: 0.00702763\n",
      "[71]\tvalid_0's l1: 0.0590467\tvalid_0's l2: 0.00702267\n",
      "[72]\tvalid_0's l1: 0.0589565\tvalid_0's l2: 0.00700425\n",
      "[73]\tvalid_0's l1: 0.0589145\tvalid_0's l2: 0.00699761\n",
      "[74]\tvalid_0's l1: 0.0588782\tvalid_0's l2: 0.00698942\n",
      "[75]\tvalid_0's l1: 0.0588522\tvalid_0's l2: 0.00698468\n",
      "[76]\tvalid_0's l1: 0.0587967\tvalid_0's l2: 0.00696597\n",
      "[77]\tvalid_0's l1: 0.0587412\tvalid_0's l2: 0.00695178\n",
      "[78]\tvalid_0's l1: 0.0586989\tvalid_0's l2: 0.00694183\n",
      "[79]\tvalid_0's l1: 0.0586632\tvalid_0's l2: 0.00693235\n",
      "[80]\tvalid_0's l1: 0.0586331\tvalid_0's l2: 0.00692453\n",
      "[81]\tvalid_0's l1: 0.0586072\tvalid_0's l2: 0.00692157\n",
      "[82]\tvalid_0's l1: 0.0585562\tvalid_0's l2: 0.00690726\n",
      "[83]\tvalid_0's l1: 0.0585418\tvalid_0's l2: 0.00690549\n",
      "[84]\tvalid_0's l1: 0.058509\tvalid_0's l2: 0.00689496\n",
      "[85]\tvalid_0's l1: 0.0584869\tvalid_0's l2: 0.0068896\n",
      "[86]\tvalid_0's l1: 0.0584263\tvalid_0's l2: 0.00687405\n",
      "[87]\tvalid_0's l1: 0.0584199\tvalid_0's l2: 0.00687463\n",
      "[88]\tvalid_0's l1: 0.0583738\tvalid_0's l2: 0.00686202\n",
      "[89]\tvalid_0's l1: 0.0583086\tvalid_0's l2: 0.00684491\n",
      "[90]\tvalid_0's l1: 0.0582755\tvalid_0's l2: 0.00683798\n",
      "[91]\tvalid_0's l1: 0.0582677\tvalid_0's l2: 0.00683774\n",
      "[92]\tvalid_0's l1: 0.0582571\tvalid_0's l2: 0.00683567\n",
      "[93]\tvalid_0's l1: 0.0582269\tvalid_0's l2: 0.00683224\n",
      "[94]\tvalid_0's l1: 0.0581795\tvalid_0's l2: 0.00682475\n",
      "[95]\tvalid_0's l1: 0.0581728\tvalid_0's l2: 0.00682114\n",
      "[96]\tvalid_0's l1: 0.0581201\tvalid_0's l2: 0.00680408\n",
      "[97]\tvalid_0's l1: 0.0581001\tvalid_0's l2: 0.00680022\n",
      "[98]\tvalid_0's l1: 0.0580769\tvalid_0's l2: 0.00679834\n",
      "[99]\tvalid_0's l1: 0.0580318\tvalid_0's l2: 0.00678817\n",
      "[100]\tvalid_0's l1: 0.0580005\tvalid_0's l2: 0.00677894\n",
      "[101]\tvalid_0's l1: 0.0579874\tvalid_0's l2: 0.00677555\n",
      "[102]\tvalid_0's l1: 0.0579807\tvalid_0's l2: 0.00677678\n",
      "[103]\tvalid_0's l1: 0.0579638\tvalid_0's l2: 0.00677095\n",
      "[104]\tvalid_0's l1: 0.0579293\tvalid_0's l2: 0.00676342\n",
      "[105]\tvalid_0's l1: 0.05791\tvalid_0's l2: 0.00676344\n",
      "[106]\tvalid_0's l1: 0.0578992\tvalid_0's l2: 0.00676211\n",
      "[107]\tvalid_0's l1: 0.0578498\tvalid_0's l2: 0.00674959\n",
      "[108]\tvalid_0's l1: 0.057827\tvalid_0's l2: 0.00674559\n",
      "[109]\tvalid_0's l1: 0.0578117\tvalid_0's l2: 0.00674041\n",
      "[110]\tvalid_0's l1: 0.0578044\tvalid_0's l2: 0.00673802\n",
      "[111]\tvalid_0's l1: 0.0577842\tvalid_0's l2: 0.00673412\n",
      "[112]\tvalid_0's l1: 0.0577507\tvalid_0's l2: 0.00672687\n",
      "[113]\tvalid_0's l1: 0.0577474\tvalid_0's l2: 0.00672595\n",
      "[114]\tvalid_0's l1: 0.0577255\tvalid_0's l2: 0.00672198\n",
      "[115]\tvalid_0's l1: 0.0577231\tvalid_0's l2: 0.00672109\n",
      "[116]\tvalid_0's l1: 0.0577055\tvalid_0's l2: 0.00672055\n",
      "[117]\tvalid_0's l1: 0.0576466\tvalid_0's l2: 0.00670775\n",
      "[118]\tvalid_0's l1: 0.057628\tvalid_0's l2: 0.00670329\n",
      "[119]\tvalid_0's l1: 0.0575997\tvalid_0's l2: 0.00669591\n",
      "[120]\tvalid_0's l1: 0.0575961\tvalid_0's l2: 0.00669584\n",
      "[121]\tvalid_0's l1: 0.0575791\tvalid_0's l2: 0.00669389\n",
      "[122]\tvalid_0's l1: 0.0575359\tvalid_0's l2: 0.0066821\n",
      "[123]\tvalid_0's l1: 0.0575284\tvalid_0's l2: 0.00667967\n",
      "[124]\tvalid_0's l1: 0.057513\tvalid_0's l2: 0.00667581\n",
      "[125]\tvalid_0's l1: 0.0574838\tvalid_0's l2: 0.00666955\n",
      "[126]\tvalid_0's l1: 0.0574864\tvalid_0's l2: 0.00666745\n",
      "[127]\tvalid_0's l1: 0.0574734\tvalid_0's l2: 0.00666472\n",
      "[128]\tvalid_0's l1: 0.0574586\tvalid_0's l2: 0.00666244\n",
      "[129]\tvalid_0's l1: 0.0574407\tvalid_0's l2: 0.00666013\n",
      "[130]\tvalid_0's l1: 0.0574323\tvalid_0's l2: 0.00665867\n",
      "[131]\tvalid_0's l1: 0.0574333\tvalid_0's l2: 0.0066599\n",
      "[132]\tvalid_0's l1: 0.0574278\tvalid_0's l2: 0.00665876\n",
      "[133]\tvalid_0's l1: 0.0574076\tvalid_0's l2: 0.00665516\n",
      "[134]\tvalid_0's l1: 0.0574125\tvalid_0's l2: 0.00665794\n",
      "[135]\tvalid_0's l1: 0.0574071\tvalid_0's l2: 0.00665589\n",
      "[136]\tvalid_0's l1: 0.0573937\tvalid_0's l2: 0.00665473\n",
      "[137]\tvalid_0's l1: 0.0573902\tvalid_0's l2: 0.00665233\n",
      "[138]\tvalid_0's l1: 0.0573902\tvalid_0's l2: 0.00665279\n",
      "[139]\tvalid_0's l1: 0.0573865\tvalid_0's l2: 0.00665121\n",
      "[140]\tvalid_0's l1: 0.0573731\tvalid_0's l2: 0.0066489\n",
      "[141]\tvalid_0's l1: 0.0573604\tvalid_0's l2: 0.0066455\n",
      "[142]\tvalid_0's l1: 0.0573624\tvalid_0's l2: 0.00664686\n",
      "[143]\tvalid_0's l1: 0.0573593\tvalid_0's l2: 0.00664809\n",
      "[144]\tvalid_0's l1: 0.0573456\tvalid_0's l2: 0.00664556\n",
      "[145]\tvalid_0's l1: 0.0573137\tvalid_0's l2: 0.006639\n",
      "[146]\tvalid_0's l1: 0.0573132\tvalid_0's l2: 0.00663865\n",
      "[147]\tvalid_0's l1: 0.0573074\tvalid_0's l2: 0.00663662\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[148]\tvalid_0's l1: 0.0573049\tvalid_0's l2: 0.00663523\n",
      "[149]\tvalid_0's l1: 0.0572911\tvalid_0's l2: 0.0066343\n",
      "[150]\tvalid_0's l1: 0.0572924\tvalid_0's l2: 0.00663432\n",
      "[151]\tvalid_0's l1: 0.0572662\tvalid_0's l2: 0.00662862\n",
      "[152]\tvalid_0's l1: 0.0572543\tvalid_0's l2: 0.00662574\n",
      "[153]\tvalid_0's l1: 0.057226\tvalid_0's l2: 0.00661834\n",
      "[154]\tvalid_0's l1: 0.0572214\tvalid_0's l2: 0.00661939\n",
      "[155]\tvalid_0's l1: 0.0572233\tvalid_0's l2: 0.00661946\n",
      "[156]\tvalid_0's l1: 0.057212\tvalid_0's l2: 0.00661578\n",
      "[157]\tvalid_0's l1: 0.0572076\tvalid_0's l2: 0.00661554\n",
      "[158]\tvalid_0's l1: 0.0571993\tvalid_0's l2: 0.00661361\n",
      "[159]\tvalid_0's l1: 0.057202\tvalid_0's l2: 0.00661621\n",
      "[160]\tvalid_0's l1: 0.0571892\tvalid_0's l2: 0.00661211\n",
      "[161]\tvalid_0's l1: 0.0571876\tvalid_0's l2: 0.00661046\n",
      "[162]\tvalid_0's l1: 0.0571866\tvalid_0's l2: 0.00661041\n",
      "[163]\tvalid_0's l1: 0.0571878\tvalid_0's l2: 0.00661\n",
      "[164]\tvalid_0's l1: 0.0571826\tvalid_0's l2: 0.00661015\n",
      "[165]\tvalid_0's l1: 0.0571824\tvalid_0's l2: 0.00661016\n",
      "[166]\tvalid_0's l1: 0.0571745\tvalid_0's l2: 0.00660962\n",
      "[167]\tvalid_0's l1: 0.0571554\tvalid_0's l2: 0.00660598\n",
      "[168]\tvalid_0's l1: 0.0571587\tvalid_0's l2: 0.00660641\n",
      "[169]\tvalid_0's l1: 0.0571557\tvalid_0's l2: 0.00660595\n",
      "[170]\tvalid_0's l1: 0.0571399\tvalid_0's l2: 0.00660777\n",
      "[171]\tvalid_0's l1: 0.057132\tvalid_0's l2: 0.00660611\n",
      "[172]\tvalid_0's l1: 0.057128\tvalid_0's l2: 0.00660516\n",
      "[173]\tvalid_0's l1: 0.0571253\tvalid_0's l2: 0.0066045\n",
      "[174]\tvalid_0's l1: 0.0571231\tvalid_0's l2: 0.00660414\n",
      "[175]\tvalid_0's l1: 0.0571209\tvalid_0's l2: 0.00660406\n",
      "[176]\tvalid_0's l1: 0.0571253\tvalid_0's l2: 0.00660481\n",
      "[177]\tvalid_0's l1: 0.0571189\tvalid_0's l2: 0.00660444\n",
      "[178]\tvalid_0's l1: 0.0571045\tvalid_0's l2: 0.00660155\n",
      "[179]\tvalid_0's l1: 0.0571037\tvalid_0's l2: 0.00660023\n",
      "[180]\tvalid_0's l1: 0.0571058\tvalid_0's l2: 0.00660109\n",
      "[181]\tvalid_0's l1: 0.0570763\tvalid_0's l2: 0.00659708\n",
      "[182]\tvalid_0's l1: 0.0570642\tvalid_0's l2: 0.00659508\n",
      "[183]\tvalid_0's l1: 0.0570605\tvalid_0's l2: 0.00659514\n",
      "[184]\tvalid_0's l1: 0.0570409\tvalid_0's l2: 0.00659086\n",
      "[185]\tvalid_0's l1: 0.0570447\tvalid_0's l2: 0.00659105\n",
      "[186]\tvalid_0's l1: 0.0570373\tvalid_0's l2: 0.00659125\n",
      "[187]\tvalid_0's l1: 0.0570329\tvalid_0's l2: 0.00659046\n",
      "[188]\tvalid_0's l1: 0.0570322\tvalid_0's l2: 0.00659034\n",
      "[189]\tvalid_0's l1: 0.0570349\tvalid_0's l2: 0.00659091\n",
      "[190]\tvalid_0's l1: 0.0570327\tvalid_0's l2: 0.00659076\n",
      "[191]\tvalid_0's l1: 0.0570249\tvalid_0's l2: 0.00658946\n",
      "[192]\tvalid_0's l1: 0.0570263\tvalid_0's l2: 0.00658941\n",
      "[193]\tvalid_0's l1: 0.0570234\tvalid_0's l2: 0.00658936\n",
      "[194]\tvalid_0's l1: 0.057018\tvalid_0's l2: 0.00658878\n",
      "[195]\tvalid_0's l1: 0.0570113\tvalid_0's l2: 0.00658768\n",
      "[196]\tvalid_0's l1: 0.0570008\tvalid_0's l2: 0.00658621\n",
      "[197]\tvalid_0's l1: 0.0570017\tvalid_0's l2: 0.00658597\n",
      "[198]\tvalid_0's l1: 0.0570015\tvalid_0's l2: 0.00658509\n",
      "[199]\tvalid_0's l1: 0.0569963\tvalid_0's l2: 0.00658326\n",
      "[200]\tvalid_0's l1: 0.0569934\tvalid_0's l2: 0.00658282\n",
      "[201]\tvalid_0's l1: 0.0569784\tvalid_0's l2: 0.00657947\n",
      "[202]\tvalid_0's l1: 0.0569634\tvalid_0's l2: 0.00657955\n",
      "[203]\tvalid_0's l1: 0.0569635\tvalid_0's l2: 0.00657958\n",
      "[204]\tvalid_0's l1: 0.0569585\tvalid_0's l2: 0.00657733\n",
      "[205]\tvalid_0's l1: 0.0569617\tvalid_0's l2: 0.0065785\n",
      "[206]\tvalid_0's l1: 0.0569614\tvalid_0's l2: 0.00657886\n",
      "[207]\tvalid_0's l1: 0.0569627\tvalid_0's l2: 0.00657942\n",
      "[208]\tvalid_0's l1: 0.0569641\tvalid_0's l2: 0.00657974\n",
      "[209]\tvalid_0's l1: 0.0569645\tvalid_0's l2: 0.00657976\n",
      "Early stopping, best iteration is:\n",
      "[204]\tvalid_0's l1: 0.0569585\tvalid_0's l2: 0.00657733\n",
      "本次结果输出的mae值是:\n",
      " 0.05695850296967709\n",
      "[1]\tvalid_0's l1: 0.244063\tvalid_0's l2: 0.0788856\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[2]\tvalid_0's l1: 0.223176\tvalid_0's l2: 0.0664122\n",
      "[3]\tvalid_0's l1: 0.204455\tvalid_0's l2: 0.0561456\n",
      "[4]\tvalid_0's l1: 0.187824\tvalid_0's l2: 0.0478148\n",
      "[5]\tvalid_0's l1: 0.173277\tvalid_0's l2: 0.0410717\n",
      "[6]\tvalid_0's l1: 0.160081\tvalid_0's l2: 0.0354211\n",
      "[7]\tvalid_0's l1: 0.148661\tvalid_0's l2: 0.0308521\n",
      "[8]\tvalid_0's l1: 0.138527\tvalid_0's l2: 0.0271285\n",
      "[9]\tvalid_0's l1: 0.129282\tvalid_0's l2: 0.0239689\n",
      "[10]\tvalid_0's l1: 0.121392\tvalid_0's l2: 0.0214621\n",
      "[11]\tvalid_0's l1: 0.114518\tvalid_0's l2: 0.0194071\n",
      "[12]\tvalid_0's l1: 0.10824\tvalid_0's l2: 0.0176404\n",
      "[13]\tvalid_0's l1: 0.102699\tvalid_0's l2: 0.0161276\n",
      "[14]\tvalid_0's l1: 0.0978181\tvalid_0's l2: 0.0149099\n",
      "[15]\tvalid_0's l1: 0.0935616\tvalid_0's l2: 0.0138972\n",
      "[16]\tvalid_0's l1: 0.0897781\tvalid_0's l2: 0.0130326\n",
      "[17]\tvalid_0's l1: 0.0865207\tvalid_0's l2: 0.0123139\n",
      "[18]\tvalid_0's l1: 0.0835797\tvalid_0's l2: 0.0116615\n",
      "[19]\tvalid_0's l1: 0.0806617\tvalid_0's l2: 0.0110192\n",
      "[20]\tvalid_0's l1: 0.0783251\tvalid_0's l2: 0.010561\n",
      "[21]\tvalid_0's l1: 0.0764453\tvalid_0's l2: 0.010213\n",
      "[22]\tvalid_0's l1: 0.0745763\tvalid_0's l2: 0.0098424\n",
      "[23]\tvalid_0's l1: 0.0730364\tvalid_0's l2: 0.0095608\n",
      "[24]\tvalid_0's l1: 0.0717259\tvalid_0's l2: 0.00932012\n",
      "[25]\tvalid_0's l1: 0.070659\tvalid_0's l2: 0.00913589\n",
      "[26]\tvalid_0's l1: 0.0695184\tvalid_0's l2: 0.00890158\n",
      "[27]\tvalid_0's l1: 0.068521\tvalid_0's l2: 0.00872442\n",
      "[28]\tvalid_0's l1: 0.0677593\tvalid_0's l2: 0.00860031\n",
      "[29]\tvalid_0's l1: 0.0670563\tvalid_0's l2: 0.00846547\n",
      "[30]\tvalid_0's l1: 0.0663721\tvalid_0's l2: 0.0083437\n",
      "[31]\tvalid_0's l1: 0.0657946\tvalid_0's l2: 0.00824613\n",
      "[32]\tvalid_0's l1: 0.0652387\tvalid_0's l2: 0.00814615\n",
      "[33]\tvalid_0's l1: 0.064785\tvalid_0's l2: 0.00807145\n",
      "[34]\tvalid_0's l1: 0.0643381\tvalid_0's l2: 0.00799664\n",
      "[35]\tvalid_0's l1: 0.0639428\tvalid_0's l2: 0.00792993\n",
      "[36]\tvalid_0's l1: 0.0636731\tvalid_0's l2: 0.0078904\n",
      "[37]\tvalid_0's l1: 0.0633901\tvalid_0's l2: 0.00784526\n",
      "[38]\tvalid_0's l1: 0.0630453\tvalid_0's l2: 0.00778262\n",
      "[39]\tvalid_0's l1: 0.0627991\tvalid_0's l2: 0.00773648\n",
      "[40]\tvalid_0's l1: 0.0625745\tvalid_0's l2: 0.00769476\n",
      "[41]\tvalid_0's l1: 0.0623287\tvalid_0's l2: 0.00765658\n",
      "[42]\tvalid_0's l1: 0.0621665\tvalid_0's l2: 0.00762669\n",
      "[43]\tvalid_0's l1: 0.0618116\tvalid_0's l2: 0.00754486\n",
      "[44]\tvalid_0's l1: 0.0615524\tvalid_0's l2: 0.0074952\n",
      "[45]\tvalid_0's l1: 0.0614\tvalid_0's l2: 0.00746806\n",
      "[46]\tvalid_0's l1: 0.0612735\tvalid_0's l2: 0.00744893\n",
      "[47]\tvalid_0's l1: 0.0611022\tvalid_0's l2: 0.00742157\n",
      "[48]\tvalid_0's l1: 0.060977\tvalid_0's l2: 0.00739853\n",
      "[49]\tvalid_0's l1: 0.0607192\tvalid_0's l2: 0.00733779\n",
      "[50]\tvalid_0's l1: 0.0605926\tvalid_0's l2: 0.00731349\n",
      "[51]\tvalid_0's l1: 0.0604936\tvalid_0's l2: 0.00729716\n",
      "[52]\tvalid_0's l1: 0.0604034\tvalid_0's l2: 0.00728515\n",
      "[53]\tvalid_0's l1: 0.0602887\tvalid_0's l2: 0.0072663\n",
      "[54]\tvalid_0's l1: 0.0601539\tvalid_0's l2: 0.00723567\n",
      "[55]\tvalid_0's l1: 0.060007\tvalid_0's l2: 0.00720224\n",
      "[56]\tvalid_0's l1: 0.0599055\tvalid_0's l2: 0.00717916\n",
      "[57]\tvalid_0's l1: 0.0598399\tvalid_0's l2: 0.00716383\n",
      "[58]\tvalid_0's l1: 0.0597649\tvalid_0's l2: 0.00715059\n",
      "[59]\tvalid_0's l1: 0.0597\tvalid_0's l2: 0.00713486\n",
      "[60]\tvalid_0's l1: 0.0595987\tvalid_0's l2: 0.00711665\n",
      "[61]\tvalid_0's l1: 0.0595196\tvalid_0's l2: 0.0071022\n",
      "[62]\tvalid_0's l1: 0.059383\tvalid_0's l2: 0.00707138\n",
      "[63]\tvalid_0's l1: 0.0592932\tvalid_0's l2: 0.00705331\n",
      "[64]\tvalid_0's l1: 0.0592234\tvalid_0's l2: 0.00704094\n",
      "[65]\tvalid_0's l1: 0.0591341\tvalid_0's l2: 0.00702208\n",
      "[66]\tvalid_0's l1: 0.059073\tvalid_0's l2: 0.00700961\n",
      "[67]\tvalid_0's l1: 0.059019\tvalid_0's l2: 0.00699905\n",
      "[68]\tvalid_0's l1: 0.0589748\tvalid_0's l2: 0.00699518\n",
      "[69]\tvalid_0's l1: 0.0589165\tvalid_0's l2: 0.00698031\n",
      "[70]\tvalid_0's l1: 0.0588849\tvalid_0's l2: 0.00697689\n",
      "[71]\tvalid_0's l1: 0.058853\tvalid_0's l2: 0.00697075\n",
      "[72]\tvalid_0's l1: 0.0588092\tvalid_0's l2: 0.00696225\n",
      "[73]\tvalid_0's l1: 0.0587735\tvalid_0's l2: 0.00695234\n",
      "[74]\tvalid_0's l1: 0.0587286\tvalid_0's l2: 0.00694158\n",
      "[75]\tvalid_0's l1: 0.0586749\tvalid_0's l2: 0.00693199\n",
      "[76]\tvalid_0's l1: 0.058612\tvalid_0's l2: 0.00691603\n",
      "[77]\tvalid_0's l1: 0.0585808\tvalid_0's l2: 0.00691155\n",
      "[78]\tvalid_0's l1: 0.0585173\tvalid_0's l2: 0.00689848\n",
      "[79]\tvalid_0's l1: 0.0584979\tvalid_0's l2: 0.0068898\n",
      "[80]\tvalid_0's l1: 0.0584677\tvalid_0's l2: 0.00688132\n",
      "[81]\tvalid_0's l1: 0.0584299\tvalid_0's l2: 0.00687103\n",
      "[82]\tvalid_0's l1: 0.0584147\tvalid_0's l2: 0.00686414\n",
      "[83]\tvalid_0's l1: 0.0583878\tvalid_0's l2: 0.00686026\n",
      "[84]\tvalid_0's l1: 0.0583368\tvalid_0's l2: 0.00684628\n",
      "[85]\tvalid_0's l1: 0.0582898\tvalid_0's l2: 0.00683886\n",
      "[86]\tvalid_0's l1: 0.0582692\tvalid_0's l2: 0.0068303\n",
      "[87]\tvalid_0's l1: 0.0582186\tvalid_0's l2: 0.0068213\n",
      "[88]\tvalid_0's l1: 0.0581802\tvalid_0's l2: 0.00681356\n",
      "[89]\tvalid_0's l1: 0.0581544\tvalid_0's l2: 0.00680582\n",
      "[90]\tvalid_0's l1: 0.0581256\tvalid_0's l2: 0.00680097\n",
      "[91]\tvalid_0's l1: 0.0580926\tvalid_0's l2: 0.00679245\n",
      "[92]\tvalid_0's l1: 0.0580769\tvalid_0's l2: 0.00678742\n",
      "[93]\tvalid_0's l1: 0.0580553\tvalid_0's l2: 0.00678329\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[94]\tvalid_0's l1: 0.0580344\tvalid_0's l2: 0.00678133\n",
      "[95]\tvalid_0's l1: 0.0580178\tvalid_0's l2: 0.0067792\n",
      "[96]\tvalid_0's l1: 0.0579945\tvalid_0's l2: 0.00677705\n",
      "[97]\tvalid_0's l1: 0.0579599\tvalid_0's l2: 0.00677002\n",
      "[98]\tvalid_0's l1: 0.0579278\tvalid_0's l2: 0.00676346\n",
      "[99]\tvalid_0's l1: 0.0578941\tvalid_0's l2: 0.00675483\n",
      "[100]\tvalid_0's l1: 0.0578671\tvalid_0's l2: 0.00675051\n",
      "[101]\tvalid_0's l1: 0.057862\tvalid_0's l2: 0.00674761\n",
      "[102]\tvalid_0's l1: 0.0578421\tvalid_0's l2: 0.00674354\n",
      "[103]\tvalid_0's l1: 0.0578054\tvalid_0's l2: 0.00673438\n",
      "[104]\tvalid_0's l1: 0.0577867\tvalid_0's l2: 0.00672863\n",
      "[105]\tvalid_0's l1: 0.057759\tvalid_0's l2: 0.00672193\n",
      "[106]\tvalid_0's l1: 0.0577344\tvalid_0's l2: 0.00671761\n",
      "[107]\tvalid_0's l1: 0.0577158\tvalid_0's l2: 0.0067133\n",
      "[108]\tvalid_0's l1: 0.0577095\tvalid_0's l2: 0.00671315\n",
      "[109]\tvalid_0's l1: 0.0576939\tvalid_0's l2: 0.00670815\n",
      "[110]\tvalid_0's l1: 0.0576888\tvalid_0's l2: 0.00670707\n",
      "[111]\tvalid_0's l1: 0.0576814\tvalid_0's l2: 0.00670523\n",
      "[112]\tvalid_0's l1: 0.0576824\tvalid_0's l2: 0.00670641\n",
      "[113]\tvalid_0's l1: 0.0576709\tvalid_0's l2: 0.00670404\n",
      "[114]\tvalid_0's l1: 0.0576689\tvalid_0's l2: 0.00670389\n",
      "[115]\tvalid_0's l1: 0.0576702\tvalid_0's l2: 0.00670487\n",
      "[116]\tvalid_0's l1: 0.057659\tvalid_0's l2: 0.00670353\n",
      "[117]\tvalid_0's l1: 0.0576338\tvalid_0's l2: 0.00669865\n",
      "[118]\tvalid_0's l1: 0.0576315\tvalid_0's l2: 0.00669768\n",
      "[119]\tvalid_0's l1: 0.057629\tvalid_0's l2: 0.00669664\n",
      "[120]\tvalid_0's l1: 0.0576168\tvalid_0's l2: 0.0066933\n",
      "[121]\tvalid_0's l1: 0.0575981\tvalid_0's l2: 0.00669042\n",
      "[122]\tvalid_0's l1: 0.0575889\tvalid_0's l2: 0.00668857\n",
      "[123]\tvalid_0's l1: 0.0575823\tvalid_0's l2: 0.00668705\n",
      "[124]\tvalid_0's l1: 0.0575609\tvalid_0's l2: 0.00668314\n",
      "[125]\tvalid_0's l1: 0.057543\tvalid_0's l2: 0.00667645\n",
      "[126]\tvalid_0's l1: 0.0575474\tvalid_0's l2: 0.00667774\n",
      "[127]\tvalid_0's l1: 0.0575349\tvalid_0's l2: 0.00667439\n",
      "[128]\tvalid_0's l1: 0.057525\tvalid_0's l2: 0.00667356\n",
      "[129]\tvalid_0's l1: 0.0575154\tvalid_0's l2: 0.00667239\n",
      "[130]\tvalid_0's l1: 0.0574792\tvalid_0's l2: 0.00666311\n",
      "[131]\tvalid_0's l1: 0.0574741\tvalid_0's l2: 0.00666224\n",
      "[132]\tvalid_0's l1: 0.057441\tvalid_0's l2: 0.00665561\n",
      "[133]\tvalid_0's l1: 0.0574263\tvalid_0's l2: 0.00665145\n",
      "[134]\tvalid_0's l1: 0.0574211\tvalid_0's l2: 0.00664973\n",
      "[135]\tvalid_0's l1: 0.0574148\tvalid_0's l2: 0.00664785\n",
      "[136]\tvalid_0's l1: 0.0574146\tvalid_0's l2: 0.0066487\n",
      "[137]\tvalid_0's l1: 0.057421\tvalid_0's l2: 0.00665064\n",
      "[138]\tvalid_0's l1: 0.0574108\tvalid_0's l2: 0.00664897\n",
      "[139]\tvalid_0's l1: 0.0574112\tvalid_0's l2: 0.00665038\n",
      "[140]\tvalid_0's l1: 0.0574058\tvalid_0's l2: 0.00665055\n",
      "Early stopping, best iteration is:\n",
      "[135]\tvalid_0's l1: 0.0574148\tvalid_0's l2: 0.00664785\n",
      "本次结果输出的mae值是:\n",
      " 0.057414793402343275\n",
      "[1]\tvalid_0's l1: 0.244063\tvalid_0's l2: 0.0788856\n",
      "Training until validation scores don't improve for 5 rounds\n",
      "[2]\tvalid_0's l1: 0.223176\tvalid_0's l2: 0.0664122\n",
      "[3]\tvalid_0's l1: 0.204455\tvalid_0's l2: 0.0561456\n",
      "[4]\tvalid_0's l1: 0.187824\tvalid_0's l2: 0.0478148\n",
      "[5]\tvalid_0's l1: 0.173277\tvalid_0's l2: 0.0410717\n",
      "[6]\tvalid_0's l1: 0.160081\tvalid_0's l2: 0.0354211\n",
      "[7]\tvalid_0's l1: 0.148661\tvalid_0's l2: 0.0308521\n",
      "[8]\tvalid_0's l1: 0.138527\tvalid_0's l2: 0.0271285\n",
      "[9]\tvalid_0's l1: 0.129282\tvalid_0's l2: 0.0239689\n",
      "[10]\tvalid_0's l1: 0.121392\tvalid_0's l2: 0.0214621\n",
      "[11]\tvalid_0's l1: 0.114518\tvalid_0's l2: 0.0194071\n",
      "[12]\tvalid_0's l1: 0.10824\tvalid_0's l2: 0.0176404\n",
      "[13]\tvalid_0's l1: 0.102699\tvalid_0's l2: 0.0161276\n",
      "[14]\tvalid_0's l1: 0.0978181\tvalid_0's l2: 0.0149099\n",
      "[15]\tvalid_0's l1: 0.0935616\tvalid_0's l2: 0.0138972\n",
      "[16]\tvalid_0's l1: 0.0897781\tvalid_0's l2: 0.0130326\n",
      "[17]\tvalid_0's l1: 0.0865207\tvalid_0's l2: 0.0123139\n",
      "[18]\tvalid_0's l1: 0.0835797\tvalid_0's l2: 0.0116615\n",
      "[19]\tvalid_0's l1: 0.0806617\tvalid_0's l2: 0.0110192\n",
      "[20]\tvalid_0's l1: 0.0783251\tvalid_0's l2: 0.010561\n",
      "[21]\tvalid_0's l1: 0.0764453\tvalid_0's l2: 0.010213\n",
      "[22]\tvalid_0's l1: 0.0745763\tvalid_0's l2: 0.0098424\n",
      "[23]\tvalid_0's l1: 0.0730364\tvalid_0's l2: 0.0095608\n",
      "[24]\tvalid_0's l1: 0.0717334\tvalid_0's l2: 0.00931933\n",
      "[25]\tvalid_0's l1: 0.0706655\tvalid_0's l2: 0.00913462\n",
      "[26]\tvalid_0's l1: 0.0695224\tvalid_0's l2: 0.0089011\n",
      "[27]\tvalid_0's l1: 0.0685251\tvalid_0's l2: 0.00872392\n",
      "[28]\tvalid_0's l1: 0.0677621\tvalid_0's l2: 0.00859975\n",
      "[29]\tvalid_0's l1: 0.0670479\tvalid_0's l2: 0.00846277\n",
      "[30]\tvalid_0's l1: 0.0663683\tvalid_0's l2: 0.00833923\n",
      "[31]\tvalid_0's l1: 0.0657891\tvalid_0's l2: 0.00824289\n",
      "[32]\tvalid_0's l1: 0.065241\tvalid_0's l2: 0.0081429\n",
      "[33]\tvalid_0's l1: 0.0647888\tvalid_0's l2: 0.00806954\n",
      "[34]\tvalid_0's l1: 0.0643411\tvalid_0's l2: 0.00799501\n",
      "[35]\tvalid_0's l1: 0.0639537\tvalid_0's l2: 0.00792677\n",
      "[36]\tvalid_0's l1: 0.0636382\tvalid_0's l2: 0.00788136\n",
      "[37]\tvalid_0's l1: 0.0631577\tvalid_0's l2: 0.00778751\n",
      "[38]\tvalid_0's l1: 0.062859\tvalid_0's l2: 0.00773368\n",
      "[39]\tvalid_0's l1: 0.0626296\tvalid_0's l2: 0.00769338\n",
      "[40]\tvalid_0's l1: 0.0624051\tvalid_0's l2: 0.00765182\n",
      "[41]\tvalid_0's l1: 0.0622352\tvalid_0's l2: 0.00762209\n",
      "[42]\tvalid_0's l1: 0.0619461\tvalid_0's l2: 0.00756547\n",
      "[43]\tvalid_0's l1: 0.0617797\tvalid_0's l2: 0.00753276\n",
      "[44]\tvalid_0's l1: 0.0614276\tvalid_0's l2: 0.00744623\n",
      "[45]\tvalid_0's l1: 0.0612517\tvalid_0's l2: 0.00742016\n",
      "[46]\tvalid_0's l1: 0.0610947\tvalid_0's l2: 0.00739592\n",
      "[47]\tvalid_0's l1: 0.0609663\tvalid_0's l2: 0.00737358\n",
      "[48]\tvalid_0's l1: 0.060769\tvalid_0's l2: 0.00733339\n",
      "[49]\tvalid_0's l1: 0.0606285\tvalid_0's l2: 0.00730529\n",
      "[50]\tvalid_0's l1: 0.0604584\tvalid_0's l2: 0.00727351\n",
      "[51]\tvalid_0's l1: 0.0602991\tvalid_0's l2: 0.00724508\n",
      "[52]\tvalid_0's l1: 0.0602221\tvalid_0's l2: 0.00722567\n",
      "[53]\tvalid_0's l1: 0.0600667\tvalid_0's l2: 0.00719851\n",
      "[54]\tvalid_0's l1: 0.0599871\tvalid_0's l2: 0.00718487\n",
      "[55]\tvalid_0's l1: 0.0598913\tvalid_0's l2: 0.00716622\n",
      "[56]\tvalid_0's l1: 0.0597944\tvalid_0's l2: 0.00715016\n",
      "[57]\tvalid_0's l1: 0.0596758\tvalid_0's l2: 0.00713336\n",
      "[58]\tvalid_0's l1: 0.0596\tvalid_0's l2: 0.00711917\n",
      "[59]\tvalid_0's l1: 0.0595392\tvalid_0's l2: 0.00710513\n",
      "[60]\tvalid_0's l1: 0.0594717\tvalid_0's l2: 0.00709746\n",
      "[61]\tvalid_0's l1: 0.0594232\tvalid_0's l2: 0.00708731\n",
      "[62]\tvalid_0's l1: 0.0593787\tvalid_0's l2: 0.00707836\n",
      "[63]\tvalid_0's l1: 0.0592889\tvalid_0's l2: 0.00705705\n",
      "[64]\tvalid_0's l1: 0.0592268\tvalid_0's l2: 0.00704876\n",
      "[65]\tvalid_0's l1: 0.0591686\tvalid_0's l2: 0.0070416\n",
      "[66]\tvalid_0's l1: 0.0590772\tvalid_0's l2: 0.00702161\n",
      "[67]\tvalid_0's l1: 0.0589662\tvalid_0's l2: 0.00699605\n",
      "[68]\tvalid_0's l1: 0.0588787\tvalid_0's l2: 0.00697071\n",
      "[69]\tvalid_0's l1: 0.0588323\tvalid_0's l2: 0.00696029\n",
      "[70]\tvalid_0's l1: 0.0587533\tvalid_0's l2: 0.00694215\n",
      "[71]\tvalid_0's l1: 0.0586919\tvalid_0's l2: 0.00692878\n",
      "[72]\tvalid_0's l1: 0.0586246\tvalid_0's l2: 0.00691109\n",
      "[73]\tvalid_0's l1: 0.0585868\tvalid_0's l2: 0.0069041\n",
      "[74]\tvalid_0's l1: 0.0585397\tvalid_0's l2: 0.00689259\n",
      "[75]\tvalid_0's l1: 0.0584929\tvalid_0's l2: 0.00688262\n",
      "[76]\tvalid_0's l1: 0.0584706\tvalid_0's l2: 0.00687733\n",
      "[77]\tvalid_0's l1: 0.0584299\tvalid_0's l2: 0.0068677\n",
      "[78]\tvalid_0's l1: 0.0583709\tvalid_0's l2: 0.00685618\n",
      "[79]\tvalid_0's l1: 0.0583309\tvalid_0's l2: 0.00684923\n",
      "[80]\tvalid_0's l1: 0.0582852\tvalid_0's l2: 0.00683956\n",
      "[81]\tvalid_0's l1: 0.0582757\tvalid_0's l2: 0.00683683\n",
      "[82]\tvalid_0's l1: 0.0582213\tvalid_0's l2: 0.00682707\n",
      "[83]\tvalid_0's l1: 0.0581754\tvalid_0's l2: 0.00681631\n",
      "[84]\tvalid_0's l1: 0.0581571\tvalid_0's l2: 0.00681542\n",
      "[85]\tvalid_0's l1: 0.0581403\tvalid_0's l2: 0.00681774\n",
      "[86]\tvalid_0's l1: 0.0581176\tvalid_0's l2: 0.00681333\n",
      "[87]\tvalid_0's l1: 0.0580879\tvalid_0's l2: 0.00680352\n",
      "[88]\tvalid_0's l1: 0.0580737\tvalid_0's l2: 0.00680133\n",
      "[89]\tvalid_0's l1: 0.0580218\tvalid_0's l2: 0.00678954\n",
      "[90]\tvalid_0's l1: 0.0580105\tvalid_0's l2: 0.00678592\n",
      "[91]\tvalid_0's l1: 0.0579909\tvalid_0's l2: 0.00678153\n",
      "[92]\tvalid_0's l1: 0.0579343\tvalid_0's l2: 0.00676701\n",
      "[93]\tvalid_0's l1: 0.0579158\tvalid_0's l2: 0.00676113\n",
      "[94]\tvalid_0's l1: 0.0578677\tvalid_0's l2: 0.00674938\n",
      "[95]\tvalid_0's l1: 0.0578526\tvalid_0's l2: 0.00674681\n",
      "[96]\tvalid_0's l1: 0.0578378\tvalid_0's l2: 0.00674361\n",
      "[97]\tvalid_0's l1: 0.057815\tvalid_0's l2: 0.00673954\n",
      "[98]\tvalid_0's l1: 0.0578017\tvalid_0's l2: 0.00673705\n",
      "[99]\tvalid_0's l1: 0.0577899\tvalid_0's l2: 0.00673524\n",
      "[100]\tvalid_0's l1: 0.057765\tvalid_0's l2: 0.0067305\n",
      "[101]\tvalid_0's l1: 0.0577622\tvalid_0's l2: 0.0067312\n",
      "[102]\tvalid_0's l1: 0.0577477\tvalid_0's l2: 0.00672642\n",
      "[103]\tvalid_0's l1: 0.0577449\tvalid_0's l2: 0.0067272\n",
      "[104]\tvalid_0's l1: 0.0577327\tvalid_0's l2: 0.00672531\n",
      "[105]\tvalid_0's l1: 0.0577088\tvalid_0's l2: 0.00672135\n",
      "[106]\tvalid_0's l1: 0.0576958\tvalid_0's l2: 0.00671529\n",
      "[107]\tvalid_0's l1: 0.0576723\tvalid_0's l2: 0.00670975\n",
      "[108]\tvalid_0's l1: 0.0576425\tvalid_0's l2: 0.00670291\n",
      "[109]\tvalid_0's l1: 0.0576288\tvalid_0's l2: 0.00670083\n",
      "[110]\tvalid_0's l1: 0.0576223\tvalid_0's l2: 0.00669825\n",
      "[111]\tvalid_0's l1: 0.0575739\tvalid_0's l2: 0.0066865\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[112]\tvalid_0's l1: 0.0575511\tvalid_0's l2: 0.00668169\n",
      "[113]\tvalid_0's l1: 0.0575504\tvalid_0's l2: 0.00668156\n",
      "[114]\tvalid_0's l1: 0.0575315\tvalid_0's l2: 0.00667443\n",
      "[115]\tvalid_0's l1: 0.0575035\tvalid_0's l2: 0.00666914\n",
      "[116]\tvalid_0's l1: 0.0574776\tvalid_0's l2: 0.00666205\n",
      "[117]\tvalid_0's l1: 0.0574713\tvalid_0's l2: 0.00666035\n",
      "[118]\tvalid_0's l1: 0.0574586\tvalid_0's l2: 0.00665749\n",
      "[119]\tvalid_0's l1: 0.057462\tvalid_0's l2: 0.00665968\n",
      "[120]\tvalid_0's l1: 0.0574539\tvalid_0's l2: 0.00665788\n",
      "[121]\tvalid_0's l1: 0.0574465\tvalid_0's l2: 0.00665674\n",
      "[122]\tvalid_0's l1: 0.0574396\tvalid_0's l2: 0.00665355\n",
      "[123]\tvalid_0's l1: 0.0574137\tvalid_0's l2: 0.00665023\n",
      "[124]\tvalid_0's l1: 0.0574152\tvalid_0's l2: 0.00665045\n",
      "[125]\tvalid_0's l1: 0.0573915\tvalid_0's l2: 0.00664429\n",
      "[126]\tvalid_0's l1: 0.0573724\tvalid_0's l2: 0.00663949\n",
      "[127]\tvalid_0's l1: 0.0573824\tvalid_0's l2: 0.00664405\n",
      "[128]\tvalid_0's l1: 0.0573845\tvalid_0's l2: 0.00664359\n",
      "[129]\tvalid_0's l1: 0.0573786\tvalid_0's l2: 0.00664196\n",
      "[130]\tvalid_0's l1: 0.0573559\tvalid_0's l2: 0.00663834\n",
      "[131]\tvalid_0's l1: 0.0573331\tvalid_0's l2: 0.00663644\n",
      "[132]\tvalid_0's l1: 0.0573199\tvalid_0's l2: 0.0066319\n",
      "[133]\tvalid_0's l1: 0.0573067\tvalid_0's l2: 0.00663149\n",
      "[134]\tvalid_0's l1: 0.057294\tvalid_0's l2: 0.00662884\n",
      "[135]\tvalid_0's l1: 0.0572883\tvalid_0's l2: 0.0066279\n",
      "[136]\tvalid_0's l1: 0.0572797\tvalid_0's l2: 0.006626\n",
      "[137]\tvalid_0's l1: 0.0572703\tvalid_0's l2: 0.00662318\n",
      "[138]\tvalid_0's l1: 0.0572728\tvalid_0's l2: 0.00662225\n",
      "[139]\tvalid_0's l1: 0.0572561\tvalid_0's l2: 0.0066201\n",
      "[140]\tvalid_0's l1: 0.0572488\tvalid_0's l2: 0.00661893\n",
      "[141]\tvalid_0's l1: 0.0572553\tvalid_0's l2: 0.00662052\n",
      "[142]\tvalid_0's l1: 0.0572455\tvalid_0's l2: 0.00662221\n",
      "[143]\tvalid_0's l1: 0.0572391\tvalid_0's l2: 0.00662034\n",
      "[144]\tvalid_0's l1: 0.0572357\tvalid_0's l2: 0.0066178\n",
      "[145]\tvalid_0's l1: 0.0572336\tvalid_0's l2: 0.00661707\n",
      "[146]\tvalid_0's l1: 0.0572169\tvalid_0's l2: 0.00661278\n",
      "[147]\tvalid_0's l1: 0.0572176\tvalid_0's l2: 0.00661231\n",
      "[148]\tvalid_0's l1: 0.0572168\tvalid_0's l2: 0.00661204\n",
      "[149]\tvalid_0's l1: 0.0572201\tvalid_0's l2: 0.00661197\n",
      "[150]\tvalid_0's l1: 0.0572184\tvalid_0's l2: 0.00661064\n",
      "[151]\tvalid_0's l1: 0.0572125\tvalid_0's l2: 0.00660915\n",
      "[152]\tvalid_0's l1: 0.0572116\tvalid_0's l2: 0.00660901\n",
      "[153]\tvalid_0's l1: 0.057203\tvalid_0's l2: 0.00660772\n",
      "[154]\tvalid_0's l1: 0.0572018\tvalid_0's l2: 0.00660706\n",
      "[155]\tvalid_0's l1: 0.0571923\tvalid_0's l2: 0.00660642\n",
      "[156]\tvalid_0's l1: 0.0571933\tvalid_0's l2: 0.00660578\n",
      "[157]\tvalid_0's l1: 0.0571993\tvalid_0's l2: 0.00660681\n",
      "[158]\tvalid_0's l1: 0.0572\tvalid_0's l2: 0.0066077\n",
      "[159]\tvalid_0's l1: 0.0571927\tvalid_0's l2: 0.00660645\n",
      "[160]\tvalid_0's l1: 0.0571965\tvalid_0's l2: 0.00660616\n",
      "Early stopping, best iteration is:\n",
      "[155]\tvalid_0's l1: 0.0571923\tvalid_0's l2: 0.00660642\n",
      "本次结果输出的mae值是:\n",
      " 0.0571923061736829\n"
     ]
    }
   ],
   "source": [
    "# max_depth\n",
    "\n",
    "scores = []\n",
    "max_depth = [3, 5, 7, 9, 11]\n",
    "\n",
    "for md in  max_depth:\n",
    "    lgbm = lgb.LGBMRegressor(boosting_type='gbdt', \n",
    "                      num_leaves=31,\n",
    "                      max_depth=md,\n",
    "                      learning_rate=0.1,\n",
    "                      n_estimators=500,\n",
    "                      min_child_samples=20,\n",
    "                      n_jobs=-1)\n",
    "    \n",
    "    lgbm.fit(X_train, y_train, eval_set=[(X_valid, y_valid)], eval_metric=\"l1\", early_stopping_rounds=5)\n",
    "    \n",
    "    y_pre = lgbm.predict(X_valid)\n",
    "    \n",
    "    mae = mean_absolute_error(y_valid, y_pre)\n",
    "    \n",
    "    scores.append(mae)\n",
    "    print(\"本次结果输出的mae值是:\\n\", mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best max_depths 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(max_depth,scores,'o-')\n",
    "plt.ylabel(\"mae\")\n",
    "plt.xlabel(\"max_depths\")\n",
    "print(\"best max_depths {}\".format(max_depth[np.argmin(scores)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.058867698663447106,\n",
       " 0.0566209902947507,\n",
       " 0.05695850296967709,\n",
       " 0.057414793402343275,\n",
       " 0.0571923061736829]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:base] *",
   "language": "python",
   "name": "conda-base-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "334px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
